Small Object Detection in Remote Sensing Based on Feature Fusion and Channel Attention

Remote sensing small target detection can accurately detect small-scale targets in remote sensing images. It is valuable for military reconnaissance and civilian image recognition. However, there are some challenges in small target detection in remote sensing images. For instance, small targets have limited feature information, making them difficult to locate. To address these issues, we propose a remote sensing small target algorithm based on feature fusion and channel attention. This algorithm aims to improve small object detection by constructing a strong feature module and fusing the receptive fields of different levels of the shallow feature map. The two shallow feature maps, which are extracted by the backbone network, are processed by the feature enhancement module, resulting in an increase in the extracted small target feature information. The feature fusion module is created using the feature map extracted from the backbone network, and the feature information of small targets is significantly enhanced after passing through the feature fusion module. To capture small target features more accurately, a new channel attention mechanism is designed. The output consists of three feature maps, and small target detection is performed on these three feature maps. Experimental results demonstrate that on the SIMD dataset and the WiderFace dataset, the proposed algorithm achieves a high average accuracy level and exhibits good robustness.