Paper Info
Title
Automobile Instrument Detection Using Prior Information and Fuzzy Sets
Abstract
Since there are many kinds of automobile instruments and the industrial environment is usually complex, automatic instrument detection during automobile production test becomes a challenging task. This article presents an automobile pointer instrument detection method based on prior information and fuzzy sets. The proposed method consists of two frameworks built around a pointer meter prior information model (PMPIM). The first one targets PMPIM construction to obtain the required prior information. With this purpose, a pointer-free template is obtained from a template generation algorithm and pointer positions are mapped into an energy function for optimization, using an energy function-based pointer positioning algorithm. The energy function is defined based on the distance between the crisp and fuzzy sets. The second framework targets PMPIM utilization to detect pointer meters during production test. A fuzzy-based image enhancement method is proposed to enhance test images and the template simultaneously. A prior information and energy function-based pointer positioning algorithm is also proposed to locate pointers in test images. Finally, the indicator value (the value the pointer points to) is calculated according to the positions of the pointer and scale marks. Experimental results show that the proposed method achieves better generalization and robustness than existing state-of-the-art methods.
Year
DOI
Venue
2022
10.1109/TIE.2021.3135523
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Keywords
DocType
Volume
Automatic optical inspection, automobile instrument detection, computer vision, fuzzy set, pattern recognition, prior information
Journal
69
Issue
ISSN
Citations 
12
0278-0046
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Jiawei Zhang100.34
Liu Yu2407.44
Jinyong Yu300.34
Xianqiang Yang400.34
Xinghu Yu500.34
J. Rodriguez-Andina623730.29
Huijun Gao78923416.93