Research on point-based object detection algorithm for small objects in point clouds

Due to the characteristics of unordered and sparse point cloud data, as well as large scale variations and diverse shapes, existing detectors have low accuracy in detecting small objects. To address the problem of difficult small object detection in point clouds, this paper proposes a Point-based Small Object Detection (PB-SOD) algorithm model. By introducing a new category-aware sampling layer, the perception ability of the model is improved, thereby enhancing the detection accuracy of small objects. At the same time, a new data augmentation strategy is proposed to improve the generalization ability of the model. Experimental results on the KITTI dataset demonstrate that the PB-SOD model achieves better detection performance for the pedestrian class, a small object category, compared to the baseline model IA-SSD and other mainstream models, effectively completing small object detection tasks on point cloud datasets.