3D Small Object Detection Algorithm Based on Dynamic Feature Enhancement

In 3D object detection from point clouds, the inherent sparsity of Light Detection And Ranging (LiDAR) data poses pronounced challenges for small objects. A small number of effective points lead to weak structural cues and blurry boundaries; limited contextual awareness hinders spatial reasoning and semantic completion, causing localization bias; and the difficulty of precise spatial localization, weak channel expressiveness, and background dominance constrain accuracy. To mitigate the impact of the aforementioned issues on detection accuracy, a dynamic-aware 3D detector is proposed that integrates dynamic feature extraction with feature-enhancement mapping, targeting two critical stages of small-object detection: feature extraction and candidate generation. Specifically, a Dynamic Point Feature Prediction Network (DPFPN) that adaptively predicts and supplements sampling points to strengthen structural perception of small objects is introduced. Subsequently, a Feature Enhancement Mapping Network (FEMN) is built that deeply fuses the original features with those produced by the dynamic module to yield context-rich 2D feature maps, thereby compensating for contextual deficiency and improving localization. Finally, a Point Cloud Feature Enhancement Network (PCFEN) module is designed to sharpen focus on key small-object regions along both channel and spatial dimensions. Experiments on the nuScenes dataset demonstrate that the proposed approach performs better than mainstream detectors. Relative to the CenterPoint baseline, the mean Average Precision (mAP) increases from 56.1% to 59.4% and the Nuscenes Detection Score (NDS) rises from 64.4 to 67.4.