Refuse Contamination Detection Using Triggered Vehicle Imaging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The mixing of different types of materials in refuse collection bins poses challenges for refuse collection and processing companies, as it complicates the separation and processing of recyclable and non-recyclable materials.
Innovation Solution
Implementing a system that uses machine learning and sensor data analysis to identify the presence of various materials in refuse, including recyclable and non-recyclable materials, by capturing images and sensor data from refuse collection vehicles, and performing actions based on contamination detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different types of materials are handled separately in refuse collection, then material separation quality is improved, but operational complexity and cost increase
Solution Approach 1:
The patent replaces manual separation operations with an automated image recognition system. Cameras capture images of refuse in containers, and machine learning algorithms automatically identify and classify different material types, eliminating the need for manual sorting while maintaining high separation accuracy.
Solution Approach 2:
The system creates a digital copy of the refuse contents through image capture and analysis. By analyzing images of materials in containers, the system can identify contamination without physically separating or handling the actual refuse, thus maintaining separation quality while reducing operational complexity.
2Measurement precision
If manual separation of recyclable and non-recyclable materials is performed, then material purity is improved, but time consumption and labor costs increase
Solution Approach 1:
The patent substitutes manual visual inspection and separation with an automated image analysis system. Cameras capture refuse containers, and machine learning models process images to identify contamination, achieving high material purity assessment without human labor or time-consuming manual sorting.
Solution Approach 2:
The system enables continuous monitoring of refuse containers throughout the collection process. Images are captured at multiple points (before pickup, during handling, after emptying), allowing continuous contamination detection without interrupting the collection workflow, thus maintaining purity while reducing time loss.
3Measurement precision
If image capture and analysis systems are deployed in refuse collection vehicles, then contamination detection accuracy is improved, but device complexity and initial costs increase
Solution Approach 1:
The patent applies multi-functionality by using the same camera and image analysis system for multiple purposes: identifying contamination types, determining material composition, tracking container handling status, and generating reports. This universal approach improves detection accuracy while managing system complexity through consolidated functionality.
Solution Approach 2:
The system performs preliminary image capture and analysis during the refuse collection process itself, before contamination issues arise. By continuously monitoring and identifying contamination early, the system can take corrective actions, improving detection accuracy while the complexity is managed through automated real-time processing rather than post-processing analysis.
4Productivity
If real-time monitoring of refuse containers is implemented, then processing efficiency is improved, but energy consumption and operational costs increase
Solution Approach 1:
The patent implements periodic image capture at key moments in the refuse collection process (when containers are picked up, when they are emptied, when contamination is detected). This periodic monitoring maintains high processing efficiency by catching contamination at critical points while reducing energy consumption compared to continuous monitoring, as images are captured only when needed based on triggering conditions.
Data Source
AI summary
A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.