Waste Collection Vehicle AI Object Detection
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Solution Overview
Problem
Current waste collection systems lack the capability to accurately differentiate between new and existing waste within the collection vehicle, leading to inefficiencies and potential safety hazards due to incorrect sorting and handling practices.
Innovation Solution
A waste management apparatus equipped with optical sensors and a machine learning module that processes images to detect and classify objects, determining new objects entering the vehicle by comparing them to a history of previous images, and sending relevant data to a remote server for further analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image processing techniques such as similarity filters are used to identify waste objects, then the system can detect objects in images, but it cannot accurately differentiate between new waste entering the vehicle and waste already present in the hopper
Solution Approach 1:
The system performs preliminary actions by capturing and storing images of waste at the moment of entry into the hopper, before the waste becomes mixed with existing waste. This preliminary documentation allows the system to later differentiate new waste from existing waste by comparing current images against the stored historical images, thereby solving the identification accuracy problem.
Solution Approach 2:
The system creates optical copies (images) of waste objects when they first enter the hopper and stores these copies for future comparison. These copied images serve as reference data that enable the system to identify whether currently detected waste objects are new or already present, resolving the information loss about waste history.
2Productivity
If waste collection vehicles compact waste regularly to improve space utilization, then the hopper capacity increases, but objects already present in the hopper may be moved, partially covered, or uncovered, causing them to be misidentified as new objects
Solution Approach 1:
The system creates and stores optical copies of waste objects at their initial position in the hopper. When compaction occurs and objects move or change appearance, the system compares current images against these stored copies using image recognition algorithms. This allows the system to recognize that moved or uncovered objects are the same as before, preventing false identification as new waste while maintaining high hopper utilization.
Solution Approach 2:
The system implements feedback by continuously comparing current waste images against stored historical images and using the comparison results to update its understanding of waste presence. This feedback mechanism allows the system to adapt to changes caused by compaction, correctly identifying whether detected objects are new or previously present, thereby maintaining measurement precision despite productivity improvements.
3Measurement precision
If the system processes and analyzes waste images in real-time to improve sorting accuracy, then the quality of waste management improves, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary image capture and basic processing when waste first enters the hopper, storing these pre-processed images for later comparison. This preliminary action reduces the computational burden during real-time operation, as the system only needs to perform comparison operations against stored images rather than full analysis, thereby maintaining sorting accuracy while reducing processing time.
Solution Approach 2:
The system creates simplified copies or feature extracts of waste images for storage and comparison, rather than storing and processing full-resolution images every time. This copying approach preserves the essential information needed for accurate sorting while significantly reducing the computational resources and time required for image processing operations.
Data Source
AI summary
At a waste management apparatus mounted on a waste collection vehicle, images of the waste dumped into a hopper are acquired. An AI module is used to detect the objects and provide, for each detected object, a position in the image and a class in a predefined classification of objects. New objects are determined amongst the detected objects based on the output of the AI module. Each object detected in the given image is compared with a history of objects detected in a plurality of previous images, based on a function of a plurality of distances comprising at least a distance between the class and a distance between the position of the objects being compared.