Abstract:

Railway subgrade defects are constantly increasing. Mud pumping and subgrade settlement are the most common defect, which serious threats railway safety operations. Vehicle-borne Ground Penetrating Radar（GPR） detection method has become the main method for railway subgrade defect detection. However, the recognition of subgrade defect by radar images is still relies on artificial recognition, requiring extensive expertise of experts. Due to the large-scale and the complex shape of defect, automatic recognition is a challenging task. In response to these problems, this paper constructs a railway subgrade defect data set by field detect of vehicle-borne ground penetrating radar, and presents a real-time intelligent detection method for railway subgrade defect（LS-YOLOv3）. This method designs the deep residual network to extract the railway subgrade defect features, and multi-scale prediction networks has been used to merge feature maps on four scales to form a real-time detection model for railway subgrade defect. The experimental results show that compared with the traditional HOG+SVM, two-stage algorithm Faster-RCNN, Cascade R-CNN, one-stagea lgorithm YOLOv3 and light weight algorithm Tiny YOLOv2, Tiny YOLOv3, algorithm proposed in this paper achieves the highest mean average accuracy（82.67%） and real-time detection（32.26 frames per second）on a computing platform equipped with a NVIDIA GeForce RTX 2080Ti GPU. This paper tries to provide a real-time new method for the detection of railway subgrade defect.

Key words: railway subgrade defect, ground penetrating radar, convolutional neural networks, YOLOv3

摘要：

铁路路基病害不断增加，其中翻浆冒泥病害和路基下沉病害最为常见，严重影响铁路安全运营。车载地质雷达检测方法是铁路路基病害检测的一种常用方法。然而，通过雷达图像对路基病害进行识别仍以人工判别为主，且需要专家丰富的经验。由于路基病害形态复杂、尺度较大，如何对铁路路基病害进行自动识别是一项具有挑战性的任务。针对这些问题，通过探地雷达实地采集数据构建了铁路路基病害数据集，提出了一种铁路路基病害实时智能检测方法（LS-YOLOv3）。该方法针对铁路路基病害的特点设计了深度残差网络提取病害特征，并采用多尺度预测网络在4个尺度上进行特征融合，形成铁路路基病害实时检测模型。实验结果表明，与传统的HOG+SVM算法、双阶段的Faster-RCNN算法、Cascade R-CNN算法、单阶段的YOLOv3算法和轻量化的TinyYOLOv2、TinyYOLOv3算法相比，提出的算法获得了最高的均值平均精度（82.67%）并在配有英伟达GeForce RTX 2080Ti GPU的计算平台上实现了实时检测（32.26 frame/s）。旨在尝试提供一种铁路路基病害检测领域的实时性新方法。

关键词: 铁路路基病害, 探地雷达, 卷积神经网络, YOLOv3