Adaptive Feature Fusion With Attention-Guided Small Target Detection in Remote Sensing Images

Small target detection in remote sensing images has considerable significance in practical applications such as military dynamic discrimination and traffic monitoring. However, the limited appearance features of small-scale targets and the widespread false alarm sources make small target detection in remote sensing images a tough challenge. To address these problems, we propose a novel small detection method by employing an adaptive multilevel feature fusion module (AMFFM) and an attention-augmented high-resolution head (AAHRH). Specifically, AMFFM is designed to suppress the interference of false alarm sources in complicated scenes. We upsample the high-level features by the context modeling of semantic information and refine the low-level features for noise removal. Then the enhanced multilevel features are fused based on the spatial and channel significance. After that, AAHRH is put forward to enhance the perception of small targets by embedding cross-dimension interaction with the attention mechanism. The prediction heads are reconstructed with high-resolution layers to improve the detection performance in densely distributed scenes. We conduct dilated and comparison experiments on a constructed small car dataset, a public small ship dataset, and the VEDAI dataset. The experimental results on two datasets verify the effectiveness and robustness of the proposed method with the state-of-the-art performance.