RC-SODet: Reparameterized dual convolutions and compact feature enhancement for small object detector
Ze Wu, Zhongxu Li, Huan Lei, Hong Zhao, Wenyuan Yang · Image and Vision Computing · 2025
In the field of object detection, small object detection tasks have broad application prospects. However, detection models often face issues with insufficient image features for small objects and limited computational resources . To address these issues, we propose RC-SODet, a small object detector that uses reparameterization techniques combined with dual convolutions and compact feature enhancement blocks. In the detector, we design Reparameterized Dual Convolutions (RepDuConv) to replace conventional convolution and downsampling blocks. Its dual-branch advantage maintains accuracy, and the reparameterization technique built on this significantly improves inference efficiency. Compact Feature-enhanced Pyramid Network (RC-FPN) serves as the neck, using reparameterizable Cross Stage Partial with Feature Fusion Reparameterized Compact Blocks (C2fRCB) for feature enhancement. First, in the backbone network , RepDuConv replaces convolution blocks to perform downsampling on input images, thereby obtaining multi-scale features to pass to the neck. Second, the model uses RC-FPN as the feature pyramid neck to process multi-scale features from the backbone. After each front-end upsampling and fusion, dual-layer C2fRCB is applied to further refine and enhance the tensor features at different fusion scales. Finally, multi-level feature maps are fused at the back-end and passed to the detection head. Additionally, in the inference stage, both RepDuConv and C2fRCB optimize branch structures through reparameterization techniques. Experimental results show that on the small object datasets VisDrone and DroneVehicle, the highest version of RC-SODet achieves 48.1% and 82.4% mAP50, as well as 30.1% and 59.1% mAP50-95, respectively. The designed reparameterization technique increases the model inference speed by 58.1%.