Skip Connection YOLO Architecture for Noise Barrier Defect Detection Using UAV-Based Images in High-Speed Railway

Noise barriers play a critical role in reducing noise and preventing foreign object from invading railway. Noise barrier structural defects such as rusted column, deteriorated mortar layer and other damages make its structure unstable, thereby threatening seriously railway operation safety. Unfortunately, existing noise barrier inspection methods still rely heavily on manual inspection, which are low-efficiency, subjective and difficult to detect the external structure of noise barriers. To solve these problems, this study proposes an automatic inspection manner for noise barrier using UAV images, and develops a fully convolutional network (FCN)-based noise barrier defect detection approach named skip connection YOLO detection network (SCYNet), which focuses on three aspects: network structure, loss function and data augmentation. First, a skip-connected feature structure Simi-BiFPN is incorporated into the network to fully fuse the features extracted from various scale layers without adding much computational overhead. Second, a NoiseIoU loss for bounding box regression is designed to improve existing IoU-based losses and get better performance on small dataset. Thirdly, a mixed sample data augmentation method named AutoFMix is proposed to eliminate the over-fitting issue caused by excessive similarity between samples, and further improve the detection accuracy. Finally, experiments conducted on the UAV railway noise barrier dataset show that the proposed SCYNet model achieves 92.2 mAP and 78.7 FPS, respectively, which outperform other models in terms of accuracy and processing speed. The fast-processing speed and high detection accuracy can quickly turn UAV images into useful information to assist railway maintenance, thereby improving the safety of train operation.