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

To deal with issues such as less feature information and difficult positioning raised by small object detection in remote sensing images, this paper proposes a remote sensing image small-target detection algorithm FFAM-YOLO (Feature Fusion and Attention Mechanism YOLO) based on feature fusion and attention mechanism. Firstly, in terms of inadequate effective information in backbone network feature extraction and weak information representation in feature maps, the algorithm constructs a feature enhancement module (FEM) to fuse multiple receptive field features in lower-level feature maps and improve the network's ability in extracting object features. Secondly, with low-level and high-level feature maps obtained by the backbone network, the algorithm's low-level and high-level feature fusion structures are rebuilt, and a feature fusion module (FFM) is implemented to enhance the feature information of small targets. Thirdly, small object features are accurately captured by cascade attention mechanism (ESM) consisting of enhanced-efficient channel attention (E-ECA) and spatial attention module (SAM). Finally, the small object is detected in the output dualbranch feature maps, and results are delivered. The experimental results show that with the USOD (Unicorn Small Object Dataset), based on the constructed remote sensing images, the proposed algorithm achieves a precision of 91. 9% and a recall of 83. 5% , with an average precision AP of 89% for intersection ratio threshold (IoU) between the prediction box and the ground truth box of 0.5 and an AP of 32.6% for IoU of 0.5 0.95, respectively, and the detection rate reaches 120 frame/s. The algorithm is with robustness and real-time performance.