Train rail defect classification detection and its parameters learning method. (February 2020) - Wu, Fupei; Li, Qinghua; Li, Shengping; Wu, Tao
Highlights: A classification detection method based on rail defect features is proposed in this paper. Based on extracted features, various defect detection models are built respectively. Adaptive adjustment models are proposed for parameter thresholds. A perceptron classification and learning model is designed to simply the detection processing. The average detection time is 0.2 s/frame, and the average detection accuracy is higher than 97%. Abstract: Many internal defects maybe arise in the rail of train, which will affect the safe driving of the high-speed railway. Considering the problem of intelligent detection of internal defects in railway, a classification detection method based on rail defect features is proposed in this paper. Firstly, rail defects features are extracted and classified based on their distribution features and contour morphological features. Then, rail defect detection models are built according to their defect features, and defects detection methods are designed. Finally, to improve detection parameters adaptability involved in the detection method, a threshold adjustment method is proposed based on their data distribution law. The proposed method can adjust the detection thresholds according to the design law to improve the detection accuracy. In addition, a data pre-screening perceptron classification and learning model is proposed for defect features of end face, weld reinforcement and lower crack of screw hole, which can transform the defects detection problem into a contour classification problem, and the detection performance can be improved by learning sample images. The rail data provided by the cooperation department are tested. Experiment results show that the detection accuracy rate of the proposed method is 97.3%, and the average detection time is 0.2 s/frame. The classifier experiment results also indicate that the proposed classifier shows good performance in recall and precision, which can be used to train and learn samples in defects detection processing.