Efficient Feature Focus Enhanced Network for Small and Dense Object Detection in SAR Images

Deep learning has demonstrated its potential capability in object detection of synthetic aperture radar (SAR) images. However, the low detection accuracy for small and dense objects remains a critical issue. To address this issue, in this work, a feature focus enhanced YOLO (FFE-YOLO) architecture is proposed. In FFE-YOLO, a channel feature enhanced (CFE) module is introduced to extract richer information and reduce time consumption by integrating it into the backbone. Additionally, a feature selection fusion network (FSFN) is designed to enhance the feature representation of small and dense objects by fully utilizing channel information. Numerical results demonstrate that FFE-YOLO outperforms baseline by 3.12% and 3.06% on datasets HRSID and LS-SSDD-v1.0, respectively, but with less inference time. These results demonstrate the effectiveness and superiority of the proposed strategy.