基于LAM-Net的轨道侵入界异物自主检测系统

针对轨道入侵异物对行车安全造成的极大威胁，而现有的轨道目标检测算法难以平衡检测精度和速度、易受复杂环境影响以及难以部署于嵌入式设备等问题，提出了一种轻量型自适应多尺度卷积神经网络，其通过特征图线性变换简化特征提取过程，使用自适应多尺度特征融合优化特征表达能力，并通过设计轻量型注意力进一步提升异物检测精度；同时，结合NVIDIA Jetson TX2嵌入式平台，研制了轨道入侵异物自主检测系统。实验结果表明，本文提出的模型很好地平衡了检测速度和精度，在NVIDIA GeForce GTX1080Ti的GPU平台上对轨道数据集的检测速度为297 FPS,检测精度为92.96%,比YOLOv4-tiny高7.72%,实现了在轨道交通复杂场景下高精度、高速度以及高鲁棒性的检测入侵异物。

Research on the autonomous detection system for railway intrusion obstacles based on LAM-Net

The railway obstacles in front of the train have great threat to traffic safety. The existing railway object detection algorithms are difficult to balance the detection accuracy and speed, which are susceptible to complex environment and difficult to deploy in embedded equipment. To address these issues, the lightweight and adaptive multiscale convolutional neural network is proposed in this article. The model simplifies the computation of redundant feature maps in feature extraction process by means of feature map linear transformation, and the adaptive multi-scale feature fusion is used to optimize the ability and further improve the accuracy of foreign obstacles detection. In addition, combined with NVIDIA Jetson TX2, an autonomous intrusion detection system for railway traffic scenes is developed. Experimental results show that the proposed model performs a great compromise between detection speed and accuracy. The detection speed of LAM-NET on the NVIDIA GeForce GTX1080Ti is 297 FPS, and the detection accuracy is 92. 96% (7. 72% higher than that of YOLOv4-tiny), which can well realize the high precision, real-time and high robustness detection for railway obstacles.