FGHDet: Delving into Fine-Grained Features with Head Selection for UAV Object Detection

Detecting small objects in unmanned aerial vehicle (UAV) imagery is a challenging and crucial task in computer vision. Most current methods struggle to address the challenges of small objects: fine-grained feature mining, multiple-layer feature fusion, and mismatches in scale between anchors and feature maps. To alleviate the aforementioned issues, we present FGHDet, which focuses on delving into fine-grained features in low-level features with a head selection mechanism. First, our approach introduces a detail-preserving semantic information enhancement module (DSIEM) to retain fine-grained information while excavating coarse-grained semantic details relevant to fine-grained information. Then, we devise a coarse-to-fine feature guidance module (CFGM) that leverages coarse-grained semantic information and fine-grained information to co-guide feature enhancement, further improving the model's classification ability. Finally, we introduce a multiscale detection strategy based on anchor-head matching, ensuring scale-level matching between anchors and feature maps to prevent overfitting due to overly fine anchor divisions. Extensive experiments on the VisDrone, CARPK, and Drone-vs.-Bird datasets demonstrate that FGHDet achieves notable improvements in $mAP$ (IoU range [0.5: 0.95]) of 4.9, 4.1, and 2.2, respectively. The code is available at https://github.com/b-yanchao/UAVDetection.git.