Research on matching algorithm for automatic testing of automotive instruments

This study addresses the challenge of automating the automotive instrument testing process, traditionally reliant on manual inspection and subjective evaluation. We propose an algorithm based on template matching, enhanced with feature-point-based image matching techniques, to improve the accuracy and reliability of automated testing for automotive instruments. By integrating classical template matching with advanced feature detection algorithms, our approach aims to overcome the limitations of traditional methods, especially in handling variations in lighting, orientation, and scale. This paper presents a comprehensive analysis of the algorithm's performance, demonstrating its potential in automating and refining the instrument testing process.

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Publication Details

Published
2024-08-16
DOI
https://doi.org/10.1117/12.3035833
Primary Topic
Industrial Vision Systems and Defect Detection
Type
article
Field-Weighted Citation Impact
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article

Research on matching algorithm for automatic testing of automotive instruments

Chunqiu Tang, Li Chen
Industrial Vision Systems and Defect Detection
article

Research on matching algorithm for automatic testing of automotive instruments

Chunqiu Tang, Li Chen
article en

Abstract

This study addresses the challenge of automating the automotive instrument testing process, traditionally reliant on manual inspection and subjective evaluation. We propose an algorithm based on template matching, enhanced with feature-point-based image matching techniques, to improve the accuracy and reliability of automated testing for automotive instruments. By integrating classical template matching with advanced feature detection algorithms, our approach aims to overcome the limitations of traditional methods, especially in handling variations in lighting, orientation, and scale. This paper presents a comprehensive analysis of the algorithm's performance, demonstrating its potential in automating and refining the instrument testing process.

Wuhan University of Technology (CN)
Openalex Percentile: Top 82%
Industrial Vision Systems and Defect Detection
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