Trains are an important means of transportation in China. With the popularity and speed increasement of trains, safety issues have received wide attention. The daily safety inspection of high-speed trains becomes crucial, the abnormal target detection for key component that is at the bottom of the train is an important part. Most of alarms which detected by machine vision based on global comparison method are false, thus, it cannot effectively monitor the key component. In this paper, the digital image processing technology is adopted to detect abnormal targets of the three key components, the steeve, the shaft cabinet and the core plate, and an algorithm is presented to detect these components of different types. The key component images are extracted from the train image by template matching. Traditional template matching method is often failed due to the strong reflection happened in the process of train bottom imaging. Therefore, the matching method based on structural similarity is proposed, which greatly improves matching accuracy. Finally, the abnormal target detection of three different key components of locomotive is realized by edge detection, shape detection and contour matching.
Edge detection is a crucial task in image processing. Owing to the similarity in property between edges and noise, which demonstrates abrupt changes in image grayscale values, traditional edge detection methods are insufficient in detecting weak edges. Therefore, a local multi-threshold fuzzy inference method (LMFI) is introduced. Considering the binarization processing prior to conducting a fuzzy inference, to retain more edge information, a local threshold processing method and a triple threshold processing method are proposed. To reduce noise interference, an improved sigma filter and an improved fuzzy inference strategy are presented. The experimental results show that the effect of weak edge detection is improved by LMFI, when compared to conventional methods such as the original fuzzy inference algorithm and Canny edge detection algorithm.
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