FSENet: Feature suppression and enhancement network for tiny object detection

Although feature fusion has been widely used to improve detection performance, it can also lead to the mixing of feature information from different layers, which affects detection of tiny objects. To alleviate feature mixing problem, suppress the complex background interference and further improve detection performance, a new feature suppression and enhancement network is designed in this paper. In order to suppress background information and object feature information from non-local feature layers, we propose a feature suppression and enhancement module (FSEM). In FSEM, feature suppression module (FSM) aims to suppress background information and redundant features while emphasizing features of tiny objects. This helps to mitigate blending of irrelevant features and increase focusing on tiny object features. Feature enhancement module (FEM) aims to highlight deep large object feature information by combining it with shallow features. By enhancing features at different scales, FEM helps maintain feature discrimination. FSM adopts a plug-and-play design and can be embedded into detectors with feature fusion capabilities. In addition, we propose an improved Kullback–Leibler divergence (IKLD) as loss function. Distribution shifting convolution (DSConv) is adopted instead of convolution in neck to reduce computational effort. The effectiveness of our method is validated on the AI-TOD, VisDrone and DOTA datasets.