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WO2024207048A1 - Procédé et appareil de tri de déchets - Google Patents

Procédé et appareil de tri de déchets Download PDF

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Publication number
WO2024207048A1
WO2024207048A1 PCT/AU2023/050274 AU2023050274W WO2024207048A1 WO 2024207048 A1 WO2024207048 A1 WO 2024207048A1 AU 2023050274 W AU2023050274 W AU 2023050274W WO 2024207048 A1 WO2024207048 A1 WO 2024207048A1
Authority
WO
WIPO (PCT)
Prior art keywords
waste
waste item
receptacle
chute
item
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/AU2023/050274
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English (en)
Inventor
Ren Ping LIU
Xu Wang
Wei Ni
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Commonwealth Scientific and Industrial Research Organization CSIRO
Original Assignee
Commonwealth Scientific and Industrial Research Organization CSIRO
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
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Priority to PCT/AU2023/050274 priority Critical patent/WO2024207048A1/fr
Publication of WO2024207048A1 publication Critical patent/WO2024207048A1/fr
Anticipated expiration legal-status Critical
Pending legal-status Critical Current

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F1/00—Refuse receptacles; Accessories therefor
    • B65F1/0033—Refuse receptacles; Accessories therefor specially adapted for segregated refuse collecting, e.g. receptacles with several compartments; Combination of receptacles
    • B65F1/0053—Combination of several receptacles
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/30—Administration of product recycling or disposal
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C2501/00—Sorting according to a characteristic or feature of the articles or material to be sorted
    • B07C2501/0054—Sorting of waste or refuse
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/34—Sorting according to other particular properties
    • B07C5/342—Sorting according to other particular properties according to optical properties, e.g. colour
    • B07C5/3422—Sorting according to other particular properties according to optical properties, e.g. colour using video scanning devices, e.g. TV-cameras
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
    • B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
    • B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
    • B07C5/36—Sorting apparatus characterised by the means used for distribution
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F1/00—Refuse receptacles; Accessories therefor
    • B65F1/0033—Refuse receptacles; Accessories therefor specially adapted for segregated refuse collecting, e.g. receptacles with several compartments; Combination of receptacles
    • B65F2001/008—Means for automatically selecting the receptacle in which refuse should be placed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/112—Coding means to aid in recycling
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/112—Coding means to aid in recycling
    • B65F2210/1125—Colors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/128—Data transmitting means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/139—Illuminating means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/144—Level detecting means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/168—Sensing means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
    • B65F—GATHERING OR REMOVAL OF DOMESTIC OR LIKE REFUSE
    • B65F2210/00—Equipment of refuse receptacles
    • B65F2210/184—Weighing means
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/06—Recognition of objects for industrial automation

Definitions

  • the present invention relates to a waste sorting method and apparatus and in one particular example, to a method and apparatus for sorting waste items for recycling by distinguishing between different types of bottles and cans.
  • Bin-e (https ://bine. world) uses an Al-based recognition system to classify recycling wastes into glass, plastic, paper and metal.
  • the smart waste bin uses a single camera in the bin to capture waste images for a recognition algorithm and achieves up to 92% accuracy.
  • the Bin-e smart waste bins can recognise plastic, they cannot differentiate different types of plastics, such as PET (polyethylene terephthalate) and HDPE (high-density polyethylene) plastic bottles, each of which follows an individual recycling process, thereby limiting its value.
  • an aspect of the present invention seeks to provide an apparatus for sorting waste items, the apparatus including: a receptacle configured to receive a waste item; one or more actuators for moving the receptacle; a plurality of sensors configured to measure waste item parameters of the waste item in the receptacle; and, one or more processing devices configured to: determine a waste item category using the waste item parameters; and, control the one or more actuators to move the receptacle and thereby transfer the waste item to one of a plurality of destinations in accordance with the waste item category.
  • an aspect of the present invention seeks to provide a method for sorting waste items, the method including: using a receptacle to receive a waste item; using one or more actuators to move the receptacle; using a plurality of sensors to measure waste item parameters of the waste item in the receptacle; and, in one or more processing devices: determining a waste item category using the waste item parameters; and, controlling the one or more actuators to move the receptacle and thereby transfer the waste item to one of a plurality of destinations in accordance with the waste item category.
  • the apparatus includes a plurality of waste bins and the one or more processing devices are configured to transfer the waste item to one of the plurality of waste bins depending on the waste item category.
  • the receptacle is a chute having a closed end.
  • the one or more actuators include: a first actuator configured to rotate the chute and thereby align the chute with one of the plurality of waste bins; and, a second actuator configured to release the waste item from the chute and thereby transfer the waste item.
  • the second actuator is configured to tip the chute and thereby release the waste item.
  • the apparatus includes: a frame; a rotation actuator attached to the frame; a post upwardly extending from the rotation actuator; a bracket movably mounted to an upper end of the post; a closed chute supported by the bracket; and, a tilting actuator attached to the bracket, wherein: the rotation actuator is configured to rotate the post and thereby align the chute with one of the plurality of bins; and, the tilting actuator is configured to move the bracket and thereby tilt the chute.
  • the apparatus includes a cover including an opening allowing a waste item to be inserted therein and positioned on the chute.
  • the one or more processing devices are configured to control the one or more actuators to align the chute with the opening to allow a waste item to be placed therein.
  • the plurality of sensors include: a visible-light camera; and, at least one of: a metal sensor; and, a weight sensor.
  • the waste item characteristic is indicative of at least one of: a quantity of metal in the waste item; a weight of the waste item; optical properties of the waste item; a reflectivity of the waste item; and, dimensions of the waste item.
  • an illumination source is provided to illuminate the waste item.
  • the visible-light camera and an illumination source are positioned above the receptacle.
  • the metal sensor is attached to the receptacle.
  • the weight sensor supports the receptacle.
  • the plurality of sensors includes one or more ultrasonic sensors configured to detect at least one of: a presence of a waste item in the receptacle; and, a fill level of a waste bin.
  • the plurality of sensors include at least one of: accelerometers;
  • the waste item characteristics include: RF signatures of materials present in the waste item; data read from RF tags associated with the waste item; and, optical properties of the waste item.
  • Figure 1A is a schematic side view of an example of an apparatus for sorting waste items
  • Figure IB is a schematic plan view of the apparatus of Figure 1A;
  • Figure 1C is a schematic diagram of an example of a processing device for performing classification and/or controlling the apparatus of Figure 1 A;
  • Figure 2 is a flow chart of an example of a process for sorting waste items
  • Figure 3 is a flow chart of a first specific example of a process for sorting waste items
  • Figure 4 is a flow chart of a second specific example of a process for sorting waste items
  • Figure 5 is a flow chart of a third specific example of a process for sorting waste items
  • Figure 6A is an image of a specific example of an apparatus for sorting waste items
  • Figure 6B is an image of the chute of the apparatus of Figure 6A;
  • Figure 7 is a flow chart of an example of operation of the apparatus of Figure 6A;
  • Figures 8A and 8B are a flow chart of an example of a control process for the apparatus of Figure 6A;
  • Figures 9A to 9P are example images of waste items captured using the apparatus of Figure 6A.
  • the apparatus 100 includes a receptacle 111, such as a chute or tray, which is configured to receive a waste item.
  • a receptacle 111 such as a chute or tray, which is configured to receive a waste item.
  • the receptacle 111 is supported by a post 112, although it will be appreciated that this is not essential and other suitable arrangements could be used.
  • One or more actuators for are provided for moving the receptacle 111 to allow waste items to be selectively dispensed therefrom.
  • the apparatus 100 includes two actuators 121, 122 allowing the receptacle 111 to be rotated and tilted, as shown by the arrows 123, 124, respectively.
  • a plurality of sensors 131, 132, 133 are provided, which are configured to measure characteristics of a waste item positioned in the receptacle 111.
  • the nature of the sensors will vary depending on the preferred implementation, although these typically include at least a visible light camera 131, and more typically a visible light camera in combination with one or both of a metal detector 132 and a weight sensor 133.
  • the apparatus 100 also includes one or more processing devices 141, which typically form part of one or more processing systems 140.
  • the processing device could be of any suitable form and could include a microprocessor, microchip processor, logic gate configuration, firmware optionally associated with implementing logic such as an FPGA (Field Programmable Gate Array), or any other electronic device, system or arrangement.
  • the waste sorting process can be performed using multiple processing devices, with processing being distributed between one or more of the devices as needed, for example using one or more processing devices to perform classification and one or more other processing devices to control the physical apparatus. Nevertheless, for the purpose of ease of illustration, the following examples will refer to a single processing device, but it will be appreciated that reference to a singular processing device should be understood to encompass multiple processing devices and vice versa, with processing being distributed between the devices as appropriate.
  • a processing system 140 can include a processing device 141, a memory 142, an optional input/output device 143, such as a keyboard and/or display, and an external interface 144, interconnected via a bus 145 as shown.
  • the external interface 144 can be utilised for connecting the processing system 140 to the sensors 131, 132, 133 and the actuators 121, 122, and optionally to other peripheral devices, such as the communications networks, databases, storage devices, or the like.
  • a single external interface 144 is shown, this is for the purpose of example only, and in practice multiple interfaces using various methods (e.g. Ethernet, serial, USB, wireless or the like) may be provided.
  • the processing device 141 executes instructions in the form of applications software stored in the memory 142 to allow the required processes to be performed.
  • the applications software may include one or more software modules, and may be executed in a suitable execution environment, such as an operating system environment, or the like.
  • the processing system 140 may be formed from any suitable processing system, such as a suitably programmed computing device, or the like.
  • the processing device is configured to determine a waste item category using sensor data from one or more of the sensors 131, 132, 133 and control the one or more actuators 121, 122 to move the receptacle 111 and thereby transfer the waste item to one of a plurality of destinations, such as waste bins 101, in accordance with the waste item category.
  • a waste item is received, for example, by having the waste item placed in the receptacle 111.
  • Sensor data can then be collected by the sensors 131, 132, 133, with this being acquired by the processing device 141 at step 210, allowing the processing device 141 to determine a waste item category at step 220.
  • the processing device 141 can sort the waste item at step 230.
  • this process includes controlling the actuator 121 to rotate the receptacle 111 as shown by the arrow 123 to align the receptacle with a waste bin 101 selected based on the determined category.
  • the actuator 122 is then controlled to thereby tilt the receptacle 111 as show by the arrow 124 so the waste item is dispensed from the receptacle into the selected waste bin 101.
  • the above described arrangement provides a compact apparatus that is capable of using data from multiple sensors to sense characteristics of waste items and use these in order to categorise and subsequently sort waste items.
  • This can be implemented using basic off the shelf components, avoiding the need for expensive equipment, such as multi- spectral cameras.
  • the use of the receptacle and actuator arrangement ensures sensing can be performed at a single location, with the waste items being easily transported to a destination, such as a waste bin, avoiding the need for complex item transporting mechanisms, such as conveyor belts, or similar.
  • the receptacle 111 is a chute, and in particular a curved chute having a closed end.
  • This allows waste items, and particularly generally cylindrical waste items, such as bottles or cans, to be placed in the chute, and rest against the closed end of the chute, preventing the waste item accidentally falling from the chute, and hence entering the wrong waste bin.
  • this can orientate waste items such as bottles and cans within the chute, to ensure these are presented consistently to the sensors, which in turn helps more accurately interpret resulting sensor data.
  • the apparatus can include a frame, which is configured to support the sorting apparatus relative to the waste bins.
  • the actuator 121 can be a rotation actuator attached to the frame, allowing the chute 111 to be aligned with the bins.
  • the post 112 extends upwardly from the rotation actuator 121, with a bracket (not shown) being rotatably mounted to an upper end of the post 112, with the chute 121 being supported by the bracket so that the actuator 122 acts as a tilting actuator to allow the chute to be tilted, thereby dispensing the waste item.
  • the apparatus can further include a cover (not shown) having an opening allowing a waste item to be inserted therein and positioned on the chute. This can be used to protect the apparatus, and prevent ingress of objects that could otherwise interfere with the sorting process.
  • the processing devices can be configured to control the one or more actuators 121, 122 to align the chute with the opening to allow a waste item to be placed therein.
  • the sensors include a visible-light camera 131, and a metal sensor 132 and/or a weight sensor 133, although in other examples, additional and/or alternative sensors may be used.
  • the visible-light camera 131 and an optional illumination source are positioned above the receptacle 111, so that the camera faces downwards to capture images of the waste item.
  • This has a number of benefits, including ensuring an uninterrupted view of the waste items, whilst allowing the waste item to rest within the receptacle 111.
  • the receptacle 111 can be coloured, for example using a black colouring, to ensure more consistent imaging, which can be assisted through the use of an illumination source to thereby counteract changes in ambient radiation. This can help ensure consistent measurement of optical properties of the waste items, and hence improve categorisation.
  • the metal sensor 132 is attached to the receptacle, thereby ensuring the metal content of the waste item is accurately measured, whilst the weight sensor 133 can support the receptacle, for example by positioning this between the rotation actuator and post, so that an accurate weight of the waste item can be captured.
  • These sensors in combination allow waste item characteristics to be measured that are indicative of a quantity of metal in the waste item, a weight of the waste item, optical properties of the waste item, such as a reflectivity of the waste item, dimensions of the waste item, text or other visual information provided on the waste item, coded data, such as bar codes, QR codes, or the like.
  • sensors could also be employed, including for example, one or more ultrasonic sensors, which can be configured to detect a presence of a waste item in the receptacle, for example to trigger the sorting process, and/or a fill level of a waste bin, for example to trigger emptying of the apparatus.
  • Other sensors for sensing waste item characteristics could include accelerometers, Radio Frequency (RF) sensors, infrared cameras and/or ultraviolet-light cameras, which can be used to measure characteristics such as the presence of RFID tags, waste material reflectivity, or the like.
  • RF Radio Frequency
  • waste items could be provided on a conveyor belt and transported past sensors, to allow categorization to be performed, with processing of sensor data again being performed by one or more processing devices as appropriate.
  • the classification of the waste items involves using a computational model, and specifically, using the sensor data and at least one computational model that is at least partially indicative of different waste item categories.
  • the computational model is typically obtained by applying machine learning to sensor data acquired from a plurality of sensors used to measure at least one reference waste item characteristic from reference waste items in different known waste item categories.
  • this process uses a computational model and sensor data from multiple sensors, typically an imaging camera and one or more of a weight sensor and metal sensor, which increases the accuracy compared to using a single sensor only.
  • multiple sensors and a computational model allows a high degree of accuracy to be achieved when categorizing waste items, allowing the apparatus to accurately sort waste items.
  • the nature of the computational model will vary depending on the preferred implementation, but could include for example a neural network such as a YOLO algorithm, which employs convolutional neural networks (CNN) to detect objects in real-time.
  • a neural network such as a YOLO algorithm, which employs convolutional neural networks (CNN) to detect objects in real-time.
  • CNN convolutional neural networks
  • the nature of the model can be of any appropriate form and could include any one or more of decision tree learning, random forest, logistic regression, association rule learning, artificial neural networks, deep learning, inductive logic programming, support vector machines, clustering, Bayesian networks, reinforcement learning, representation learning, similarity and metric learning, genetic algorithms, rule-based machine learning, learning classifier systems, or the like. As such schemes are known, these will not be described in any further detail.
  • the processing device applies at least some of the sensor data to the computational model.
  • An example of this, for the specific embodiment of neural networks will now be described with reference to Figure 3.
  • a waste item is received, with sensor data being collected by the sensors, with this being acquired by the processing device at step 310.
  • the processing device applies the sensor data to the neural network computational model, with this providing an indication of a categorisation at step 330.
  • sensor data such as image data from an imaging device
  • the method may include determining one or more metrics from the sensor data and then applying the metrics to the at least one computational model.
  • the image data could be analysed, for example by performing edge detection to determine dimensions of the waste item, with the dimensions then being applied to the computational model.
  • image data could be analysed to determine other metrics, such as the reflectivity of the waste item material, the presence and content of any text or images on the waste item, the presence or content of coded data, such as barcodes, QR codes, or the like.
  • a single computational model is used, and sensor data from each of the plurality of sensors is applied to the one model.
  • a respective computational model could be used for each sensor, so that sensor data and/or metrics derived from the sensor data associated with one sensor can be applied to a respective computational model.
  • image data could be applied to an image computational model, weight sensor data applied to a weight computational model, and so on.
  • each model can then be used to provide an indication of a possible categorisation, with these being combined in some manner to determine a waste item category.
  • outputs from the sensors could be assessed sequentially, which can streamline the analysis in some instances.
  • the processing device could assess sensor data from a first sensor to determine if the waste item can be categorised. If so, the waste item is categorised, and if not sensor data from at least one further sensor is assessed, with this process continuing until a category can be determined.
  • An example of this is shown in more detail in Figure 4.
  • a waste item is received, with sensor data being collected by the sensors and acquired by the processing device at step 410.
  • the processing device selects a next sensor, and attempts to perform categorisation at step 430.
  • This can involve the use of a computational model, although this may not be required, depending on the nature of the sensor and the waste items. For example, when sorting bottles and cans, typically only cans are metallic, and so if more than a certain amount of metal is detected, this can be used to determine the waste item is a can.
  • a waste item is received, with sensor data being collected by the sensors and acquired by the processing device at step 510.
  • the processing device analyses sensor data, and then determines a number of category probabilities at step 530. This can be performed for individual sensors, for example, determining separate probabilities using metal and weight sensors. Additionally, and/or alternatively, this could be performed using combinations of sensors, for example determining one probability using a combination of image data and metal sensor data and another probability using the combination of image data and weight sensor data.
  • the probabilities can be used to determine a waste item category at step 540. This can be achieved in different manners depending on the preferred implementation, for example by determining a waste item category based on the categorisation indication having a highest probability or by selecting a categorisation indication exceeding a threshold.
  • the apparatus 600 includes a camera system 631 positioned above the chute, allowing the camera to image waste items positioned on the chute 611, and example captured images are shown in Figures 9A to 9P.
  • a light 631.1 is positioned proximate the camera 631, which in this example is in the form of a ring light surrounding the camera 631, which helps ensure even illumination of the waste item. This minimises the impact of variable ambient light, and thus ensures the waste sample is consistently illuminated, irrespective of external illumination sources. It will be appreciated that this can be further assisted by the use of an opaque cover.
  • the processor system 640 uses an NVIDIA Jetson device to perform Al-based plastic classification, while the camera module uses a Raspberry Pi camera to capture video of waste items.
  • the light module uses a ring light to provide additional illumination, and the apparatus uses an electrician device to control the sensors and servos.
  • An object and fullness detector module uses the ultrasonic sensors 634, 635 to detect waste items and measure the fullness of the bins, while a scale module uses the weight sensor 633 to measure the weight of waste items.
  • a metal detector module uses the metal sensor 632 to detect metal waste items, and a movement module uses servos 621, 622 to move the chute 611.
  • the rotation-tilt-based sorting mechanism is more compact and suitable for confined space.
  • the rotation-tilt-based sorting can also keep deposited waste bottles at a fixed point making it easy to design the locations of the sensors.
  • the rotation-tilt-based in-situ sorting mechanism can also be extended in a few ways. For example, more bins can be added to accept more types of wastes, with the rotation angles being updated accordingly. Additional sets of bins can be provided, with the chute 611 being moved horizontally to a different set of bins. A chute or splash guard can be added to assure bottles are always dumped into the bin.
  • the automatic smart bin consistently monitors the chute 611 with the ultrasonic sensor 634 to detect when a waste item has been received at step 700. Once the processing system 640 detects a waste item being deposited onto the chute 611, the processing system 640 will start the sensing, classification and sorting process.
  • the apparatus senses the waste with the metal sensor(s) 632 installed on the chute 611, and the weight sensor 633 under the chute 611 at step 730, as well as using the camera 631 on top of the chamber to image the waste item, capturing multimedia sensor data at step 710.
  • Sensor data is sent to the Al classification module implemented by the processing system 640, which analyses the video stream to perform object detection at step 720, and then combines with other sensor data to classify the waste.
  • the Al classification module analyses the sensor data from the sensing module(s), including the multimedia data and non-multimedia data.
  • the multimedia data such as video stream
  • the vision-based object detection model can identify PET bottles in the video stream, and then the size of the bottles and the confidence of the detection result.
  • the multimedia-based object detection module can use the off-the-shelf vision-based object detection models. This allows the apparatus to accurately recognise waste types, including the fine recognition on plastic types, with multiple sensing data.
  • the apparatus can also employ computer vision technologies, including object detection, image classification, Optical Character Recognition (OCR), and bar/QR code scanners, to recognise plastic types.
  • OCR Optical Character Recognition
  • the detection module can employ the OCR technology to extract the brand and other information on the bottle label, and could also include a bar/QR code scanner to read the product code on the bottle if there is any code appearing in the image.
  • the automatic smart bin further refines recognition accuracy with multiple sensing data, including visible light signals, ultraviolet-light signals, near infrared light signals, weight signals, ultrasonic signals, and metal signals.
  • Al vision models have been used to classify images according to image content and identify objects in the image, but has yet to classify different types of plastics with similar appearance.
  • To classify different plastics a large data set with a variety of plastics is created.
  • massive photo data sets of different types of plastics are taken at different angles and labelled with the corresponding plastic types, as shown for example in Figures 9A to 9P.
  • the photos are taken using the automatic smart bin with the same background and lighting condition.
  • the dataset is then used to train the object detection model such that the object detection model can classify different plastics.
  • the output of the multimedia-based object detection and other non-multimedia data are fed into the classification algorithm to get the final waste type detection result at step 740.
  • the data will also be sent to a waste item status algorithm at step 750 to determine a bottle status.
  • An example of classifying a PET bottle is as follows.
  • the object detection model suggests the object could be PET or glass from the video stream; the classification algorithm can calculate the density of the bottle using the size information from the object detection model and the weight from the weight sensor, and then confirm the bottle is type PET.
  • the detection results and non-multimedia data are also sent to the status description algorithm for recycle analysis.
  • the algorithm can describe the bottle as a Brand- A’ product-P bottle with half full of water.
  • the brand and product information can be deduced from the multimedia-based object detection algorithm, while the half full of water is deduced from the weight and the acceleration data.
  • the control algorithm receives the classification at step 800. If it is determined the waste is classified to be metal at step 805, the control algorithm controls the chute 611 to rotate to the metal bin direction, at step 810 and then tilts the chute 611 to dump the waste at step 840. Meanwhile, the control algorithm reads the fullness of the metal bin with the ultrasonic sensor on top of the bin at step 845 and optionally updates the bin fullness on the screen 643, or uploads an indication to a remote location, such as a monitoring system that coordinates bin emptying. Next, the control algorithm tilts up the chute 611 and rotates the chute 611 to the initial position at step 850, and resets the sensors for the next task at step 855.
  • the control algorithm determines whether the waste is glass at step 815, or HDPE or PET at step 825 (in each case rotating the chute 611), and then sorts the waste to appropriate bins at steps 820, 830 or 835, respectively, before performing steps 840 to 855 as required.
  • the focus has been on classification of glass, metal cans, PET bottles and HDPE bottles.
  • the techniques can be adapted to recognize and classify a wider range of waste items, including food containers, coffee cups, paper, cardboard, and other types of plastics.
  • the classification algorithm can be trained with a large number of samples at a variety of statuses, to help ensure that the module is able to accurately and reliably classify waste items, regardless of their condition or appearance. This can help improve the overall performance and effectiveness of the automatic smart bin technology.
  • the current version of the apparatus only contains four bins (glass, metal, PET and HD PE) centred around the chute 611. This is due to the limited size of the automatic smart bin and because of the fact that the rotation-based sorting mechanism can only handle this number of bins. As a result, the current version of the automatic smart bin is limited in its ability to sort waste items into the four categories. However, it will be appreciated that this can be expanded to enable sorting of a greater variety of waste items.
  • the above arrangement seeks to provide an apparatus, and in one example, a smart bin, that:
  • the sensing module(s) should be able to allocate deposited waste regardless of how the waste is deposited into the bin, such that the sensing module(s) can control physical variables during sensing.
  • the apparatus can automatically classify and sort a variety of types of waste bottles without human intervention.
  • the apparatus provides a drop-and-go waste classification and sorting on-field solution. Individuals can simply deposit their waste bottles and other items into the apparatus, and it will automatically classify and sort the items into the appropriate containers or bins for recycling or disposal.
  • Waste management Smart bins can be used in waste management facilities to automatically classify and sort waste bottles and other items into the appropriate containers or bins for recycling or disposal. This can help improve the efficiency and accuracy of waste sorting and recycling processes.
  • Smart bins can be installed in public spaces, such as parks, malls, and schools, to provide individuals with a convenient way to dispose of waste bottles and other items.
  • the automatic classification and sorting capabilities of the smart bin can help reduce litter and improve waste management in these areas.
  • Smart bins can be used in manufacturing facilities to automatically classify and sort wastes and other items generated during the production process. This can help improve the efficiency and sustainability of manufacturing operations.
  • the apparatus can play an important role in implementing effective waste reduction and recycling strategies.
  • the automatic smart bin technology contributes to reducing waste, improving the recycling rate, reducing recycling costs, and improving sustainability in the waste management industry.
  • the classification and sorting technology in the automatic smart bin can also be used in other fields, e.g., recycling leftover material during manufacturing.

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Abstract

L'invention concerne un appareil de tri d'articles de déchets, l'appareil comprenant un réceptacle configuré pour recevoir un article de déchets, un ou plusieurs actionneurs pour déplacer le réceptacle, une pluralité de capteurs configurés pour mesurer des paramètres d'articles de déchets de l'article de déchets dans le réceptacle et un ou plusieurs dispositifs de traitement. Le ou les dispositifs de traitement sont configurés pour déterminer une catégorie d'articles de déchets à l'aide des paramètres d'articles de déchets et commander le ou les actionneurs pour déplacer le réceptacle et transférer ainsi l'article de déchets à l'une d'une pluralité de destinations conformément à la catégorie d'articles de déchets.
PCT/AU2023/050274 2023-04-05 2023-04-05 Procédé et appareil de tri de déchets Pending WO2024207048A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5423431A (en) * 1989-03-23 1995-06-13 Sellsberg Engineering Ab Method and an apparatus for waste handling
US6124560A (en) * 1996-11-04 2000-09-26 National Recovery Technologies, Inc. Teleoperated robotic sorting system
US10464105B2 (en) * 2014-08-13 2019-11-05 Metrosense Oy Method, apparatus and system for sorting waste
US11471916B2 (en) * 2015-07-16 2022-10-18 Sortera Alloys, Inc. Metal sorter

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5423431A (en) * 1989-03-23 1995-06-13 Sellsberg Engineering Ab Method and an apparatus for waste handling
US6124560A (en) * 1996-11-04 2000-09-26 National Recovery Technologies, Inc. Teleoperated robotic sorting system
US10464105B2 (en) * 2014-08-13 2019-11-05 Metrosense Oy Method, apparatus and system for sorting waste
US11471916B2 (en) * 2015-07-16 2022-10-18 Sortera Alloys, Inc. Metal sorter

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