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.