Fast detection method of railway foreign object intrusion based on deep learning
The intrusion of foreign objects poses a great risk to the safety of the train and its passengers,and these objects can easily cause unexpected accidents. To address this problem,this paper proposed a rapid detection method by integrating track boundary detection with intrusion foreign object detection,employing the popular YOLOv3 model that was widely used in the industrial field. Firstly,the ResNet-18 network was used as the backbone,and an auxiliary detection module was added to improve detection accuracy. This approach could achieve fast feature extraction and capture sufficient semantic information. Simultaneously,a segmentation algorithm based on row anchor frames was used to detect the coordinates of the track lines. This article combined it with the definition of the railway foreign object invasion limit under standard gauge to reduce the detection area for intrusion objects. Secondly,an intra-layer multi-scale residual module based on Octave convolution was designed to convert the single-channel convolution into dual-channel convolution,significantly reducing the computational cost of convolution and further improving the detection speed. Finally,a spatial pyramid module and a feature adaptive fusion module were introduced to facilitate the exchange of high-level and low-level semantic information,thereby enhancing the network's ability to perceive targets at different scales and reducing semantic conflicts. The accuracy,speed,and effectiveness of the foreign object intrusion detection algorithm are verified through comparative experiments. The experimental results demonstrate that the proposed method achieves a detection speed of 172 frames per second,with an accuracy of 98.12％. Compared with other algorithms,the proposed method can achieve twice the speed of the YOLOv3 algorithm while maintaining accuracy. It also surpasses other comparison algorithms in detecting targets at large,medium,and small scales. The method can meet the real-time detection requirements for intrusion objects,and provide an efficient and accurate solution to enhance train safety.
万方数据
维普
