Delving Into Coarse-Fine Feature Interaction Alignment for UAV Object Detection

Abstract

Due to limited features and dense object layouts, object detection in UAV images is challenging. Given that existing feature fusion methods have not fully explored the relationship between fine- and coarse-grained features, direct feature fusion can result in poor correlation between them, hindering the representative capability of fine-grained semantic information. To alleviate this issue, we introduce a method of Coarse-fine Feature Interaction Alignment (CFIA), which enhances the correlation between coarse-grained and fine-grained features across multi-scale feature maps through their interactive alignment. Firstly, we present the Wavelet-based High-frequency Preserving Down-sampling (WHPD), utilizing wavelet transform to extract high-frequency information to enhance object boundaries, minimizing crucial fine-grained information loss. Secondly, we propose the Feature Refinement and Interaction Alignment Strategy (FRIAS), which achieves feature interaction alignment by establishing the association of feature maps between coarse-grained and fine-grained features. This enhances the representative capability of feature maps at various scales for detecting small objects. Extensive experiments on the VisDrone, CARPK, and Drone-vs-Bird datasets have demonstrated the effectiveness of the CFIA method, which is highly competitive with state-of-the-art methods. The code is available at https://github.com/b-yanchao/CFIA.git.