Feature aggregation network for small object detection

Due to the miniature scale and limited identifiable features, small objects pose a significant challenge in detection. Improving the accuracy of small object detection is a momentous issue of concern among researchers. Feature pyramid network employs a divide-and-conquer strategy for detecting small objects in low-level networks. However, the limited semantic information in these networks results in suboptimal performance in small object detection. To address this issue, we fully utilize information from all feature levels and propose a Feature Aggregation Network (FAN). We investigate information propagation pathways in neural networks, analyze early fusion and late fusion of features, and introduce a dual top-down pathway that utilizes high-level semantic information to consistently reinforce low-level spatial information. We design a Feature-Aware Module that narrows the semantic gap and steers the network toward learning features that favor small object detection. We employ deformable convolution to accurately locate the boundaries of objects with varying shapes and sizes. FAN can function as a plug-and-play component with minimal computational overhead and be trained end-to-end alongside backbone networks. Extensive experiments are conducted on the COCOs, TinyPerson, and VisDrone datasets. The highly competitive results demonstrate that our approach exhibits robust generalization capabilities and can further improve the accuracy of small object detection.