Small Targets Detection in LIDAR Point Clouds Based on Deep Learning

A multifeature fusion small-target detection network (MF-Net) is proposed based on PointRCNN, aimed at enhancing the detection accuracy of small targets in vehicle-mounted LiDAR systems. Semantically controlled farthest point sampling and multisampling strategies are presented to achieve uniform sampling and retain a greater number of small target points. Additionally, a local feature aggregation module is utilized to learn the intensity features of small target point clouds through spatial intensity encoding. Furthermore, PointPillars technology is implemented to convert the three-dimensional point cloud into a pseudo-image, allowing for the extraction of features at various scales using a feature pyramid network. Experimental results demonstrate that MF-Net improves the mean average precision for pedestrian and cyclist detection by 2.49% and 2.88%, respectively, compared to the baseline network PointRCNN. The false detection rate is reduced significantly and the detection accuracy is enhanced across diverse scenarios.