Skip Connection YOLO Architecture for Noise Barrier Defect Detection Using UAV-Based Images in High-Speed Railway
Skip Connection YOLO Architecture for Noise Barrier Defect Detection Using UAV-Based Images in High-Speed Railway
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DOI：
10.1109/tits.2023.3292934
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发表时间：
2023-11
影响因子：
8.5
通讯作者：
Jingwen Cui;Yong Qin;Yunpeng Wu;Changhong Shao;Huaizhi Yang
中科院分区：
文献类型：
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作者：
Jingwen Cui;Yong Qin;Yunpeng Wu;Changhong Shao;Huaizhi Yang
隔声屏障在降低铁路噪声和防止外来物侵入方面具有重要作用。隔声屏障结构缺陷如柱锈蚀、砂浆层变质等损伤使其结构不稳定，严重威胁铁路运营安全。遗憾的是，现有的噪声屏障检测方法仍然严重依赖人工检测，效率低，主观，难以检测到噪声屏障的外部结构。针对这些问题，本研究提出了一种利用无人机图像对噪声屏障进行自动检测的方法，并开发了一种基于全卷积网络（FCN）的噪声屏障缺陷检测方法——跳跃连接YOLO检测网络（SCYNet），该方法主要从网络结构、损失函数和数据增强三个方面进行研究。首先，在网络中引入一种跳跃连接的特征结构Simi-BiFPN，在不增加计算开销的情况下，充分融合各尺度层提取的特征；其次，设计了用于边界盒回归的NoiseIoU损失，以改进现有的基于ou的损失，并在小数据集上获得更好的性能。再次，提出了一种混合样本数据增强方法AutoFMix，消除了样本间过于相似导致的过拟合问题，进一步提高了检测精度。最后，在无人机铁路噪声屏障数据集上进行的实验表明，所提出的SCYNet模型分别达到了92.2 mAP和78.7 FPS，在精度和处理速度上都优于其他模型。处理速度快，检测精度高，可以将无人机图像快速转化为辅助铁路维修的有用信息，从而提高列车运行的安全性。
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.