Vehicle Component Image Matching Under Wear and Breakage
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Solution Overview
Problem
Existing image recognition methods struggle to accurately identify specific vehicle components when they are worn, broken, or dirty, as they often require exact matches and fail to account for varying conditions such as wear and breakage.
Innovation Solution
A control system that compares images of an object to be identified with a plurality of reference images of known objects under different conditions, including wear and breakage, using 3D models and machine learning algorithms to determine candidate objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image recognition methods are used to identify objects, then the identification process is simple and fast, but the accuracy deteriorates when objects are worn, broken, or dirty
Solution Approach 1:
The system performs preliminary actions by capturing multiple images of the object from different angles and conditions before identification. It also pre-processes these images to enhance features and create a comprehensive object representation, allowing accurate identification even when the object is worn or damaged.
Solution Approach 2:
The system changes parameters by analyzing multiple imaging parameters including different angles, lighting conditions, and magnifications. It transforms the object representation into various feature spaces (geometric, textural, color) to find the most suitable matching parameters for identification despite object degradation.
2Measurement precision
If multiple reference images at different conditions are used to improve identification accuracy, then the matching accuracy improves, but the data processing complexity increases
Solution Approach 1:
The system segments the complex identification task into multiple independent steps: capturing images from different conditions, pre-processing each image, extracting specific features (geometric, textural, color), comparing features separately, and synthesizing results. This segmentation manages complexity while maintaining high accuracy.
Solution Approach 2:
The system introduces intermediary elements including multiple reference images under varying conditions, feature extraction intermediaries that transform raw images into comparable parameters, and a multi-stage comparison process. These intermediaries facilitate accurate matching while organizing the complexity of handling diverse object conditions.
3Measurement precision
If exact matches are required for object identification, then the identification precision is high for new objects, but the system fails to identify worn or damaged components
Solution Approach 1:
The system transitions from static exact matching to dynamic matching that adapts to object conditions. It dynamically adjusts matching criteria based on detected wear or damage patterns, allowing the identification threshold to flex rather than requiring rigid exact matches, thereby maintaining reliability across varying object states.
Solution Approach 2:
The system performs preliminary analysis to detect the condition of the object (new, worn, damaged) before final identification. This preliminary assessment allows the system to adjust its matching strategy accordingly, ensuring reliable identification whether the object is in pristine or degraded condition.
Data Source
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AI summary
Aspects of the present invention relate to a control system for determining one or more candidate objects, an electronic device, and a method for determining one or more candidate objects. The control system comprises one or more processors collectively configured to: receive one or more images of an object to be identified; compare the one or more images of the object to a plurality of reference images corresponding to a plurality of known objects, the plurality of reference images comprising images of each of the known objects at a plurality of conditions; determine, in dependence on a similarity between the object to be identified and at least one reference image of at least one known object, one or more candidate objects from the plurality of known objects as potential matches with the object to be identified; and output an indication of the one or more candidate objects.