Abnormal target detection for key components of locomotive based on image processing

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.