Automated Waste Receptacle Detection Using Gradient Response Maps
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing waste collection systems struggle to efficiently identify and pick up un-marked and textureless waste receptacles in natural environments, such as those with foliage, due to the reliance on specific markings or guide elements.
Innovation Solution
A system comprising a camera, processor, and arm-actuation module that captures images, generates pose candidates, and verifies them against template representations using gradient-response maps and histogram of oriented gradients (HOG) to automatically locate and move waste receptacles, even in complex environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional camera systems are used to detect waste receptacles, then the system can identify marked receptacles, but it fails to detect un-marked and textureless receptacles in natural environments
Solution Approach 1:
The system transforms the detection approach by changing parameters from relying on color, texture, or markings to detecting geometric parameters such as gradients, curves, and shapes at the receptacle boundary. This parameter transformation enables detection of un-marked and textureless receptacles in natural environments by focusing on invariant geometric features rather than appearance-based features.
Solution Approach 2:
The system performs preliminary processing of the image by generating gradient-response maps and identifying candidate regions before final verification. This preliminary action filters out background elements and focuses computational resources on potential receptacle locations, improving both detection accuracy and environmental adaptability.
2Productivity
If manual waste collection is performed, then operators can handle various receptacle types, but labor costs increase and collection speed decreases
Solution Approach 1:
The system enables self-service automation by using the camera and processing algorithms to automatically locate, identify, and guide the mechanical arm to waste receptacles without continuous human intervention. The automated detection and verification system performs the detection task independently, reducing labor requirements while maintaining high collection speed.
Solution Approach 2:
The system replaces manual mechanical operations with an automated vision-guided mechanical arm system. The camera-based detection system substitutes human visual inspection and decision-making with automated image processing algorithms, while the mechanical arm substitutes manual receptacle handling with automated grasping and transport mechanisms.
3Measurement precision
If five reflective marks are attached to garbage cans for detection, then the camera can easily detect the pattern, but the system cannot detect un-marked receptacles and the markings increase manufacturing complexity
Solution Approach 1:
The system extracts the detection task from relying on artificial markings and instead detects intrinsic geometric features of the receptacles themselves. By taking out the requirement for reflective marks and focusing on natural geometric boundaries, the system achieves detection of un-marked receptacles while simplifying manufacturing requirements.
Solution Approach 2:
Instead of making the receptacles detectable by adding markings (traditional approach), the system inverts the approach by making the detection system sensitive to geometric features that naturally exist on all receptacles regardless of markings. This inversion eliminates the need for additional receptacle modifications while maintaining detection accuracy.
Data Source
AI summary
Systems and methods for detecting and picking up a waste receptacle, the system being mountable on a waste-collection vehicle, and including an arm for grasping the waste receptacle, a processor, a camera, a non-transitory computer-readable medium, and an arm-actuation module. The processor is configured for generating a pose candidate based on an image captured by the camera, verifying that the pose candidate matches a template representation stored on the medium, and calculating a location of the waste receptacle. The arm-actuation module can be configured to automatically move the arm in response to the calculated location, in order to grasp the waste receptacle, lift, and dump the waste receptacle into a waste-collection vehicle.