Abstract:

Existing deep learning models are difficult to meet the real-time performance of high-speed detection, and a structured region fully convolutional neural network (SR-FCN) is proposed. In order to meet the task of real-time detection of high-speed comprehensive inspection vehicles, considering that the rails, rail fasteners, track plates and other facilities in the track image are relatively fixed, their position distribution can constitute a unique structural feature of the track scene, structured detection region is set, the detection of small rail fastener targets in an image is converted into a large target region detection with a fixed structure, and the detection of small rail fastener targets is transformed into a positioning problem in a structured region, which can speed up the network training convergence speed, reduce the number of candidate regions generated, thereby greatly improving the detection speed. The struc-tured prior information of the railway track is integrated into the various processes of the deep learning network, which effectively improves the positioning accuracy to ensure the robustness of the detection. The results of labor-atory offline analysis and on-site online testing show that the proposed SR-FCN obtains 99.99% and 99.84% detec-tion accuracy, respectively, and maintains a relatively fast detection speed, which can meet the 350 km/h real-time detection requirements.

Key words: target detection, deep learning, rail fasteners, structured scenes, high-speed inspection

摘要：

现有的深度学习模型很难满足高速检测的实时性，针对性地提出了一种结构化区域全卷积神经网络（SR-FCN）。为了满足高速综合巡检车实时检测的要求，考虑到轨道图像中钢轨、扣件、轨道板等设施位置相对固定，其位置分布可以构成轨道场景特有的结构化特征，因此设定了结构化检测区域，将一幅图像中扣件小目标的检测转化为一整块具有固定结构的大目标区域检测，将扣件小目标的检测问题转化为结构化区域的定位问题，可加快网络的训练收敛速度，减少候选区域的生成个数，从而大幅提高检测速度。将铁路轨道的结构化先验信息融合到深度学习网络的各个过程中，有效提高了定位精度，保证了检测的鲁棒性。实验室离线分析和现场在线检测的结果表明，所提出的SR-FCN网络分别获得了99.99%和99.84%的检测精度，同时还保持了较快的检测速度，可以满足350 km/h的实时检测要求。

关键词: 目标检测, 深度学习, 扣件, 结构化场景, 高速巡检