A multi-scale enhanced feature fusion model for aircraft detection from SAR images
Guoqing Zhou, Ziqi Zhang, Feng Wang, Qiang Zhu, YueFeng Wang, Ertao Gao, Yufu Cai, Xiao Zhou, Cong Li
Abstract
Aircraft detection has been a challenging task although many efforts have been made due to the diversity of aircraft scale and interference of complicated background in synthetic aperture radar (SAR) images. So, this paper proposes a new method, named ‘multi-scale enhanced feature fusion network, briefly, MSEFF-Net’. Firstly, a nonlinear activation free attention module (NAFAM) is proposed to enhance the feature information of aircraft. Secondly, a feature fusion module (FFM) is designed and a multi-scale feature fusion pyramid network (MFFPN) is proposed to integrate the semantic information of different layers. Finally, a global-to-local context aggregation (GLCA) module is built to aggregate global and local information. The proposed model is validated using two groups of public datasets, SAR-AIRcraft-1.0 and SADD, and is compared with various advanced detection methods (e.g. Faster R-CNN, Cascade-RCNN, YOLO series and RT-DETR series). The experimental results demonstrate that the precision, the recall, and the mAP50 reach 97.4%, 97.6%, 98.9% for SADD dataset; the mAP50 and the mAP50:95 reach 70.1% and 49.0% for SAR-AIRcraft-1.0 dataset, respectively. The results indicate that the proposed method achieves higher accuracy than the other detection methods do.