Road falling objects are an important factor affecting driving safety and causing traffic jams. It is crucial to promptly detect and clean up road litter. By summarizing and analyzing the characteristics of falling objects, using the characteristics of high positioning accuracy of point cloud processing and high classification accuracy of image processing, a highway falling object detection algorithm based on image and point cloud fusion is proposed. The proposed algorithm includes the following three steps: road object extraction, point cloud and image information fusion, and falling object classification. First, the laser radar point cloud is used to detect the road edge, filter the ground point cloud, and cluster the point cloud to extract the target point cloud clusters on the road; second, the target point cloud cluster is projected into the time and space-aligned visual image to obtain the corresponding image region of interest; finally, the optimized ResNet-50 is used for target classification. The average detection accuracy rate of the proposed algorithm is 94.84%, and the recall rate is 91.92%, which has a good detection effect.