基于YOLOv3算法的中低速列车在途障碍物检测方法

Detection Method of Obstacles of Medium-low Speed Train in Transit Based on YOLOv3 Algorithm

针对中低速列车在途障碍物检测,提出一种基于YOLOv3算法的障碍物检测手段,首先通过深度学习算法,有效识别场景中的障碍物;再利用基于freeman链码的边缘检测算法,提取列车轨道边缘,从而判定障碍物是否影响行车,并对司机做出警示。同时,通过迁移学习的方式,扩充YOLOv3网络数据集,以达到提升特定场景下本方法对目标障碍物识别准确度的目的。实验结果表明,本方法具有较高的适用性,是一种便捷、高效的在途列车障碍物检测方法。

In this paper,an obstacle detection method based on YOLOV3 algorithm is proposed for obstacle detection of medium-low speed trains in transit.First,the deep learning algorithm is used to eff ectively identify obstacles in the scene.Then,the edge detection algorithm based on Freeman chain code is used to extract the edge of the train track,so as to determine whether the obstacles aff ect the traffi c and give a warning to the driver.At the same time,the YOLOV3 network data set is expanded by means of transfer learning,so as to achieve the purpose of improving the target obstacle recognition accuracy by use of this method in a specifi c scene.The experimental results show that this method has high applicability and is a convenient and effi cient obstacle detection method for trains in transit.