Methods and arrangements to aid recycling

A method comprising the acts:

• compiling historical conveyor belt map data derived from images depicting a conveyor belt loop at positions throughout a full cycle of conveyor belt travel without waste thereon;
• after compiling said historical conveyor belt map data, capturing first imagery depicting a first region of the conveyor belt with waste thereon;
• by comparison with the historical conveyor belt map data, identifying a first set of conveyor belt area blocks depicted in the first imagery in which the conveyor belt is visible and a second set of conveyor belt area blocks depicted in the first imagery in which the conveyor belt is not visible, said second set of area blocks including a first clump of adjoining area blocks;
• providing imagery depicting said first clump of adjoining conveyor belt area blocks to a convolutional neural network, and receiving an output from the convolutional neural network indicating that said first clump of adjoining area blocks is occupied by a single waste item only;
• controlling a diverter mechanism to act on a diversion target within said first clump of adjoining conveyor belt area blocks, to remove said single waste item to a repository;
• after compiling said historical conveyor belt map data, capturing second imagery depicting a second region of the conveyor belt with waste thereon;
• by comparison with the historical conveyor belt map data, identifying a first set of conveyor belt area blocks depicted in the second imagery in which the conveyor belt is visible and a second set of conveyor belt area blocks depicted in the second imagery in which the conveyor belt is not visible, said second set of area blocks including a second clump of adjoining area blocks;
• providing imagery depicting said second clump of adjoining conveyor belt area blocks to the convolutional neural network, and receiving an output from the convolutional neural network indicating that said second clump of adjoining area blocks is occupied by more than one waste item;
• and
• not controlling a diverter mechanism to act on a diversion target within said second clump of adjoining area blocks.

A method comprising:

• compiling historical conveyor belt map data derived from images depicting a conveyor belt loop at positions throughout a full cycle of conveyor belt travel without waste thereon;
• after compiling said historical conveyor belt map data, capturing first imagery depicting a first region of the conveyor belt with waste thereon;
• by comparison with the historical conveyor belt map data, identifying a first set of conveyor belt area blocks depicted in the first imagery in which the conveyor belt is visible and a second set of conveyor belt area blocks depicted in the first imagery in which the conveyor belt is not visible, said second set of area blocks including a first clump of adjoining area blocks;
• providing imagery depicting said first clump of adjoining conveyor belt area blocks to a convolutional neural network, and receiving an output from the convolutional neural network indicating that said first clump of adjoining area blocks is occupied by a single waste item only;
• and
• controlling a diverter mechanism to act on a diversion target within said first clump of adjoining conveyor belt area blocks, to remove the single waste item to a repository.

A system comprising:

• at least one optical sensor that provides sensor data sensed from waste on a conveyor belt, the sensor data including imagery;
• means for determining first attribute information, from the sensor data, for waste at a first location on said conveyor belt, in which the first attribute information comprises a digital watermark payload or a material type or a food/non-food type;
• a multi-layer convolutional neural network configured by weights, learned through training, that causes the convolutional neural network to classify the imagery as depicting (a) a single waste item, or (b) two or more adjoining or overlapping items;
• and
• a waste diverter system for said conveyor belt, configured to activate only if the convolutional neural network classifies the imagery as depicting (a) a single waste item.

A system comprising:

• means for compiling historical conveyor belt map data derived from images depicting a conveyor belt loop at positions throughout a full cycle of conveyor belt travel without waste thereon;
• means for capturing first imagery depicting a first region of the conveyor belt with waste thereon, after compiling said historical conveyor belt map data;
• means for identifying a first set of conveyor belt area blocks depicted in the first imagery in which the conveyor belt is visible and a second set of conveyor belt area blocks depicted in the first imagery in which the conveyor belt is not visible, the second set of area blocks including a first clump of adjoining area blocks, in which said means for identifying utilizes a comparison with the historical conveyor belt map data;
• means for providing imagery depicting said first clump of adjoining conveyor belt area blocks to a convolutional neural network, and means for receiving an output from the convolutional neural network indicating that said first clump of adjoining area blocks is occupied by a single waste item only;
• and
• means for controlling a diverter mechanism to act on a diversion target within said first clump of adjoining conveyor belt area blocks, to remove the single waste item to a repository.