CNN Waste Receptacle Detection for Automated Collection
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Solution Overview
Problem
Current waste collection systems struggle to efficiently detect and differentiate between various types of waste receptacles, especially un-marked and textureless ones in natural environments, and fail to accurately identify and locate multiple receptacles with similar shapes and colors, leading to inefficiencies in labor and collection speed.
Innovation Solution
A system equipped with a camera and a convolutional neural network (CNN) mounted on a waste collection vehicle, which captures images, generates object candidates, and determines their classification as garbage, recycling, or compost, using pixel coordinates and class confidence scores to guide an arm-actuation module for automatic grasping and dumping, while also distinguishing between multiple receptacles based on their features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual waste collection methods are used, then labor flexibility is maintained, but collection speed and productivity are low
Solution Approach 1:
The system enables automatic waste receptacle detection and identification without continuous human intervention. The CNN-based detection system autonomously processes images, generates object candidates, and identifies waste receptacles, allowing the collection vehicle to operate with minimal manual input while maintaining high productivity
Solution Approach 2:
The patent replaces manual visual inspection and manual operation with an automated computer vision system. The CNN-based detection system substitutes human operators' visual processing capabilities, enabling faster and more consistent waste receptacle identification while reducing labor requirements
2Measurement precision
If simple object detection methods are used, then system complexity is reduced, but detection precision and accuracy are insufficient
Solution Approach 1:
The patent introduces an intermediary processing layer between image capture and waste receptacle identification. The object candidate generation module serves as a mediator that pre-processes images, identifies potential regions of interest, and prepares data for the CNN classifier, thereby improving detection accuracy while managing system complexity through modular architecture
Solution Approach 2:
The detection system is segmented into distinct functional modules: image capture, object candidate generation, CNN-based classification, and waste receptacle identification. This segmentation allows each module to specialize in specific tasks, improving overall detection precision while enabling independent optimization and maintenance of individual components
3Quantity of substance
If multiple waste receptacles with similar features are present, then collection capacity increases, but detection and differentiation become more difficult
Solution Approach 1:
The CNN-based detection system applies local quality analysis by examining specific regional features within each detected object candidate. The system evaluates local patterns, textures, and characteristics within bounding boxes to differentiate between waste receptacles, enabling accurate identification even when receptacles have similar overall appearances
Solution Approach 2:
The system changes detection parameters by adjusting the CNN model's sensitivity thresholds, confidence levels, and feature weighting. By dynamically modifying detection parameters based on scene complexity and receptacle density, the system maintains high differentiation accuracy when multiple similar receptacles are present
Data Source
AI summary
Systems and methods for detecting a waste receptacle, the system including a camera for capturing an image, a convolutional neural network, and processor. The convolutional neural network can be trained for identifying target waste receptacles. The processor can be mounted on the waste-collection vehicle and in communication with the camera and the convolutional neural network configured for using the convolutional neural network. The processor can be configured for using the convolutional neural network to generate an object candidate based on the image; using the convolutional neural network to determine whether the object candidate corresponds to a target waste receptacle; and selecting an action based on whether the object candidate is acceptable.