Abstract
Railway transportation has always occupied an important position in daily life and social progress. In recent years, computer vision has made promising breakthroughs in intelligent transportation, providing new ideas for detecting rail lines. Yet the majority of rail line detection algorithms use traditional image processing to extract features, and their detection accuracy and instantaneity remain to be improved. This paper goes beyond the aforementioned limitations and proposes a rail line detection algorithm based on deep learning. First, an accurate and lightweight RailNet is designed, which takes full advantage of the powerful advanced semantic information extraction capabilities of deep convolutional neural networks to obtain high-level features of rail lines. The Segmentation Soul (SS) module is creatively added to the RailNet structure, which improves segmentation performance without any additional inference time. The Depth Wise Convolution (DWconv) is introduced in the RailNet to reduce the number of network parameters and eventually ensure real-time detection. Afterward, according to the binary segmentation maps of RailNet output, we propose the rail line fitting algorithm based on sliding window detection and apply the inverse perspective transformation. Thus the polynomial functions and curvature of the rail lines are calculated, and rail lines are identified in the original images. Furthermore, we collect a real-world rail lines dataset, named RAWRail. The proposed algorithm has been fully validated on the RAWRail dataset, running at 74 FPS, and the accuracy reaches 98.6%, which is superior to the current rail line detection algorithms and shows powerful potential in real applications.
Highlights
As an essential national infrastructure, railway transportation has received significant attention from society for its safety [1]
The rail line detection algorithm based on computer vision can be divided into two directions: one is based on the image processing algorithms, using image edge detection and other algorithms to search for rail lines features and curve fitting
The other is based on deep convolutional neural networks, which have powerful semantic information extraction capabilities to obtain advanced feature information such as the edge, color, and texture of the rail lines and segment the railway tracks and background face of more complex images information
Summary
As an essential national infrastructure, railway transportation has received significant attention from society for its safety [1]. In realizing the intelligentization and automation of railway transportation, the primary task is to predict the railway tracks in front during operation to provide trains with basic information about the environment ahead in time [10]. In this way, the train can sense the track’s condition in advance, and adjust the speed in time, so as to avoid rail traffic accidents such as speeding and derailment in the curve. The rail lines area is detected in advance to prevent foreign matter intrusion, which can help frame the detection range and reduce the amount of processing In this way, the operation safety of the train can be ensured in real-time. The other is based on deep convolutional neural networks, which have powerful semantic information extraction capabilities to obtain advanced feature information such as the edge, color, and texture of the rail lines and segment the railway tracks and background face of more complex images information
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