CNN Waste Receptacle Detection for Automated Collection Sorting
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Solution Overview
Problem
Current waste collection systems face challenges in efficiently detecting and distinguishing between different types of waste receptacles, especially those without markings or in textured environments, which hampers automation and increases labor costs.
Innovation Solution
A system equipped with a camera and a convolutional neural network (CNN) mounted on a waste collection vehicle, capable of capturing images, identifying target waste receptacles, and determining their location for automatic pickup and sorting, using object classification and bounding box regression with a MobileNet architecture and single shot detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual waste collection methods are used, then labor flexibility is maintained, but labor costs increase and collection speed decreases
Solution Approach 1:
The waste collection system performs detection and identification automatically without continuous human intervention. The CNN-based detection system operates autonomously to identify waste receptacles and guide the mechanical arm, enabling the system to serve itself in the detection and navigation tasks, thereby improving collection speed while managing automation complexity.
Solution Approach 2:
The patent replaces manual mechanical operations with an automated system comprising a CNN-based detection system, image processing algorithms, and a mechanically controlled arm. This substitution of manual mechanical systems with automated optical and mechanical systems resolves the contradiction by improving productivity through automation while containing complexity through integrated system design.
2Measurement precision
If marked waste receptacles are used, then detection accuracy improves, but implementation complexity and cost increase
Solution Approach 1:
The patent extracts the detection task from requiring physical markers on waste receptacles and instead uses natural visual features of the receptacles themselves. The CNN-based system processes images to identify waste receptacles based on their inherent visual characteristics, eliminating the need for additional markers while maintaining detection accuracy and reducing implementation complexity.
Solution Approach 2:
The system creates a digital representation (image) of the waste receptacle and processes this copy through CNN algorithms to identify the receptacle. This approach allows detection based on visual copies rather than physical markers, improving ease of manufacture while maintaining measurement precision through advanced image processing.
3Productivity
If automated arm control is implemented, then collection efficiency improves, but system complexity and initial costs increase
Solution Approach 1:
The waste collection system implements feedback loops where the CNN detection system continuously monitors the environment, provides real-time identification information to the arm control system, and adjusts arm movements based on detected receptacle positions. This feedback mechanism enables efficient automated operation while managing complexity through iterative control based on actual system state.
Solution Approach 2:
The automated arm system is designed to perform multiple functions including approaching waste receptacles, grasping them, lifting, and depositing into the collection container. This multi-functionality improves collection efficiency by consolidating operations into a single automated system while managing complexity through integrated design that handles various tasks with one mechanism.
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.