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Small object detection is essential for robot navigation, especially for avoiding vulnerable pedestrians. Usually, the points assigned to small objects in Lidar scans are sparse; detecting them efficiently and accurately is still a challenging problem. This paper proposes a real-time and accurate small object detection method (ScorePillar) based on the pillar point scoring mechanism, which focuses on the relationship among points in pillars. Considering that voxel-based object detection methods are not efficient enough for real-time application, compact pillar-based structures are leveraged to represent Lidar scans for improving efficiency. For better extraction of multi-scale features on pillar projection of point cloud, a ResNet-based feature extraction module is combined with an attention block and multi-dilation atrous convolutions to improve efficiency and accuracy further. Extensive experiments on the KITTI and nuScenes datasets show the validity and efficiency of ScorePillar. Note that ScorePillar achieves a 3.5% improvement in mAP detecting pedestrian objects on the KITTI dataset and first place in the average mAP among Lidar-only methods. Code is publicly available at: https://github.com/Cao-Zonghan/ScorePillar.
Cao et al. (Mon,) studied this question.