YOLOSR-IST: A deep learning method for small target detection in infrared remote sensing images based on super-resolution and YOLO

Infrared remote sensing imaging has a wide range of military and civilian applications. The detection of dim small targets is one of the most valuable research topics in this field. However, model-driven methods are not robust enough to noise, target size and contrast in images, and the currently proposed deep learning methods have insufficient ability to process and fuse important features, resulting in more missed detections and false alarms in these methods. To solve these problems, in this paper, a detection method based on super-resolution and deep learning is proposed. First, we use super-resolution preprocessing and multiple data augmentation on the input images. Secondly, based on the characteristics of infrared small target, we propose a new deep learning network named YOLOSR-IST. This network is based on a series of improvements on YOLOv5, including adding Coordinate Attention to backbone, introducing a high-resolution feature map P2 in the feature fusion, and replacing bottleneck layer of the C3 module in the head of the network with Swin Transformer Blocks. The proposed method achieves [email protected] of 99.2% and 94.6% on two public datasets respectively, and solves the problem of missed detections and false alarms more effectively compared with current advanced data-driven detection methods.