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.
Image segmentation is the most fundamental part of computer vision, which is the foundation of all other methods of image processing. The quality of image segmentation technology will affect the subsequent processing considerably. Comparing with traditional image segmentation algorithms, image segmentation algorithm based on deep learning is constantly proposed, with high performance and efficiency. But there is also a lot of room for improvement. For example, key parts such as fastening bolt are usually small in size, polluted and covered, and do not have enough characteristic information, so it is difficult to obtain satisfactory results. These factors affect the accuracy of the test, which is easy to cause serious accidents. As traditional methods sometimes cannot meet the requirement of high-accuracy result, deep learning play a particularly important role in facing those problems. To solve the problem that traditional object recognition methods are not robust enough to extract image features, parts recognition accuracy is low, and segmentation is not possible, we have made some modifications based on Mask R-CNN. In this method, convolutional neural network is used to extract features from part images. Then we use some annotated images from dataset to fine-tuned Mask R-CNN network to guarantee the accuracy. At the same time, data enhancement and k-folding cross-validation are carried out to improve the robustness of the model. Finally, the result of part recognition and segmentation by building the experimental platform proves the significance of the method.
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