Automatic detection system for spring hook of automobile seat back based on feature detection and linear detection

Shengnan Sun; Juan Zhu; Jipeng Huang; Lianming Wang; Xu Gao; Jinhuan Li

doi:10.1117/12.2505143

8 February 2019 Automatic detection system for spring hook of automobile seat back based on feature detection and linear detection

Shengnan Sun, Juan Zhu, Jipeng Huang, Lianming Wang, Xu Gao, Jinhuan Li

Author Affiliations +

Proceedings Volume 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging; 108430F (2019) https://doi.org/10.1117/12.2505143
Event: Ninth International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT2018), 2018, Chengdu, China

Abstract

To identify the accuracy of the installation position of the spring hooks on both sides of the automobile seat back curtain automatically, this paper presents an automatic detection system for spring hooks on automobile seat back based on feature detection and line segment. Firstly, the backrest image of the automobile is matched by a scale-invariant feature transformation algorithm (SIFT algorithm) to classify different types of automobile seat backs. Secondly, an edge detection method is used to extract the characteristic thread to locate the middle line of the spring hook (or parallel lines) and the side line where the spring hook hooks. Finally, the possibility of the intersection of the spring middle line (or parallel lines) and side line to be inside the side line slot is calculated to determine qualification level of the automobile seats. The experimental results indicated that the system detection accuracy was up to 98.8%. The system meets the practical requirement of automobile industry with high efficiency.

Citation Download Citation

Shengnan Sun, Juan Zhu, Jipeng Huang, Lianming Wang, Xu Gao, and Jinhuan Li "Automatic detection system for spring hook of automobile seat back based on feature detection and linear detection", Proc. SPIE 10843, 9th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optoelectronic Materials and Devices for Sensing and Imaging, 108430F (8 February 2019); https://doi.org/10.1117/12.2505143

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KEYWORDS

Image processing

Detection and tracking algorithms

Image processing algorithms and systems

Edge detection

Image filtering

Gaussian filters

Image segmentation

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