A Multi-Scale Spatio-Temporal Fusion Network for Occluded Small Object Detection in Geiger-Mode Avalanche Photodiode LiDAR Systems

The Geiger-Mode Avalanche Photodiode (Gm-APD) LiDAR system demonstrates high-precision detection capabilities over long distances. However, the detection of occluded small objects at long distances poses significant challenges, limiting its practical application. To address this issue, we propose a multi-scale spatio-temporal object detection network (MSTOD-Net), designed to associate object information across different spatio-temporal scales for the effective detection of occluded small objects. Specifically, in the encoding stage, a dual-channel feature fusion framework is employed to process range and intensity images from consecutive time frames, facilitating the detection of occluded objects. Considering the significant differences between range and intensity images, a multi-scale context-aware (MSCA) module and a feature fusion (FF) module are incorporated to enable efficient cross-scale feature interaction and enhance small object detection. Additionally, an edge perception (EDGP) module is integrated into the network’s shallow layers to refine the edge details and enhance the information in unoccluded regions. In the decoding stage, feature maps from the encoder are upsampled and combined with multi-level fused features, and four prediction heads are employed to decode the object categories, confidence, widths and heights, and displacement offsets. The experimental results demonstrate that the MSTOD-Net achieves mAP50 and mAR50 scores of 96.4% and 96.9%, respectively, outperforming the state-of-the-art methods.