A fusion detection method using multi line lidar point cloud detection combined with image recognition analysis is proposed to address the hazards of obstacles such as railway landslides and falling rocks. After clustering based on Euclidean distance, it is proposed to apply real scene restriction information to filter out interference clustering, while combining inter frame information to greatly reduce the false alarm rate of point cloud detection. Image detection uses YOLO-V5 for object detection and proposes to use a whole target sequence to achieve target information matching. On the basis of unifying the coordinate system of radar and camera, a "Intersection small ratio" method is proposed to achieve the fusion of camera and radar detection results. The research results indicate that the obstacle detection method combining point cloud and image can accurately and reliably detect people, trains, and various types of obstacles within the region, providing new technical support for railway line state perception.
Visual extraction of railway is widely used in automatic driving and obstacle detection. At present, there are many research results on lane line detection. However, there is a big difference between lane line detection and railway extraction. Rail extraction faces many challenges, such as complex environments and complex rail shapes. This paper introduces a railway extraction method based on sequential line detection. In this method, picture is divided into multiple stages from bottom to top. According to proposed matching score, select lines that are most matching to railway in each stage are selected. Then, all detected lines are linked as final extracted railway, which makes proposed method is effective to straight railway and curved railway. Experiments have proved that proposed method remains accurate and stable in different environments.
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