An Anomaly Detection Method for Railway Track Using Semisupervised Learning and Vision-Lidar Decision Fusion

Anomaly detection of the railway track is essential to protect the safety of railway transportation. However, the railway track is often disturbed by unknown anomalies, making it challenging to detect anomalies using only supervised learning and visual sensors. To address these problems, this article proposes a railway track anomaly detection method using semisupervised learning and vision-lidar decision fusion. The method consists of three parts: supervised pre-detection, semisupervised re-detection, and decision-level fusion. In the pre-detection stage, an enhanced instance segmentation algorithm is used to pre-detect known and unknown anomalies to improve the overall performance. In the re-detection stage, the reconstruction method, semisupervised learning, and structured light point cloud are combined to re-detect anomalies, especially unknown anomalies. In the fusion stage, the fusion of pre-detection and re-detection at the decision level further advances the accuracy and credibility of anomaly detection. Experimental results show that the proposed method achieves a mean average precision (mAP 0.50:0.95) of 92.2% and a mean average recall (mAR 0.50:0.95) of 94.5%. The proposed method is superior to the single vision-based or point cloud-based methods in anomaly detection. In addition, the proposed method provides a general solution to anomaly detection and extends the application of structured light point cloud.