Target Detection in Remote Sensing Images Based on Recognition Algorithm

Aiming at the problem of low target detection accuracy caused by the close arrangement of targets, complex feature information, and numerous small targets in remote sensing images, this paper proposes a target detection algorithm based on the improved Yolov5 algorithm for remote sensing images. Firstly, this paper designs a remote sensing image target detection algorithm C²-YOLO based on multi-level feature fusion. Combined with the attention mechanism module to focus on the spatial location information of the target, which improves the extraction effect of the features of the target of interest. The network uses the CARAFE upsampling operator with a larger sensing field to better utilize the surrounding information. The proposal is to incorporate a predictive header hl that is generated from a low-level, high-resolution feature map to improve the recognition of tiny objects. The problem of large target scales in remote sensing images and poor detection of small targets has been improved. Angular loss is also added to the loss function to improve the detection of rotating targets. Finally, the accuracy comparison and experimental effect visualization are carried out on the dataset, and the experimental results show that the improvement work in this paper achieves better results no matter comparing the original YOLOv5 detection algorithm or other types of detection algorithms. Specifically, the accuracy of C²-YOLO on the NWPU dataset 1-shot recognition task is 78.45%, which is much higher than that of other detection algorithms, and is able to reach a superior performance in the remote sensing image target detection task.