A Semantic Feature Enhancement-Based Aerial Image Target Detection Method Using Dense RFB-FE

Aerial image target detection is a challenging task due to the complex backgrounds, dense target distribution, and large-scale differences often present in aerial images. Existing methods often struggle to effectively extract detailed features and address the issue of imbalanced positive and negative samples. To tackle these challenges, an aerial image target detection method (dense RFB-FE-CGAM) based on dense RFB-FE and channel-global attention mechanism (CGAM) was proposed. First, the authors design a shallow feature enhancement module using dense RFB feature multiplexing and expand convolution within an SSD network, improving detailed feature extraction. Second, they introduce CGAM, a global attention module, to enhance semantic feature extraction in backbone networks. Finally, they incorporate a focal loss function for joint training, addressing sample imbalance. In experiments, the method achieved an mAP of 0.755 on the DOTA dataset and recall/AP values of 0.889/0.906 on HRSC2016, confirming the effectiveness of dense RFB-FE-CGAM for aerial image target detection.