FPN with GMM Based Feature Enhancement Strategy for Object Detection in Remote Sensing Images

In the realm of object detection, the age-old challenge of accommodating large variations in target scales, particularly in the intricate domain of remote sensing imagery, has long perplexed computer vision aficionados. Feature Pyramid Network (FPN) family, a widely-used stalwart, strives to tame this scale variation challenge by harmoniously fusing features across different levels. However, this typical feature fusion strategy often leads us astray. Noise introduction and feature smoothing problems due to different semantic information from high/low resolution feature maps, which results in semantic misalignment and inconspicuous gradient discrepancy between targets and background. This, in turn, leads to the difficulty in locating and distinguishing target from complex background in remote sensing images. In this paper, a GMM Feature Enhancement Module (GFEM) is proposed to address the problem by generating and enhancing feature of target with Gaussian Mixture Model (GMM), hence avoiding the gradient smoothing problem. Moreover, we introduce a generic feature fusion network named GFEM-FPN, elevating our approach to the next level. GFEM-FPN extracts multi-scale target enhancement features to enhance the ability of discriminating targets and background. The proposed methods are evaluated on NWPU VHR-10 and DIOR-R datasets, and the outperformance in results verify the effectiveness of the proposed method.