Affiliations: Engineering Investigation and Survey Design Institute, China Railway Shanghai Design Institute Group Corporation Limited, Shanghai, China
Correspondence:
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Corresponding author: Xianzheng Fan, Engineering Investigation and Survey Design Institute, China Railway Shanghai Design Institute Group Corporation Limited, Shanghai 200333, China. E-mail: [email protected].
Abstract: Railway transportation is the main means of transportation for people and the main way of logistics transportation, playing an important role in daily life. Therefore, the safety inspection of railway track has been widely valued. The abnormal intelligent detection of rail fasteners is the key content of rail safety detection. The traditional rail fastener detection method is based on machine learning for image recognition, such as SVM, to detect abnormal rail fasteners. But the traditional method has two defects. The first point is that the detection time is long, and the second point is that the detection accuracy is low. To solve this problem, a rail fastener anomaly detection model based on SVM optimized by IFOA algorithm is proposed. Firstly, the image of rail fastener is collected and filtered; Then, edge detection and image segmentation are performed to obtain the image of the target area; Finally, the HOG feature and LBP feature of the image are extracted, and the improved IFOA-SVM is used to recognize and classify the features, so as to achieve intelligent rail fastener anomaly detection. The experimental results show that when the IACO-SVM model is iterated to 254 times, the fitness value tends to be stable, which is 0.24. The detection accuracy of the model reaches 99.82%, which is higher than the traditional models, and can meet the work requirements of rail fastener anomaly detection. The rail fastener anomaly detection model based on SVM can improve the efficiency of rail fastener anomaly detection, and has a positive effect on the normal operation of railway transportation. However, the number of experimental samples used in the study is limited, which may lead to some errors in the experimental results. Therefore, it is necessary to increase the number of samples in subsequent studies.
