Infrared small target detection algorithm with U-shaped multiscale transformer network

To solve the problem of small targets feature extraction and the susceptibility of targets to being overwhelmed by noise and complex backgrounds, a detection method with U-shaped multiscale transformer network is proposed. Based on the U-shaped multiscale network architecture, the proposed method uses convolution operations to extract and enhance local salient features of small targets. Concurrently, it uses the Transformer mechanism to model global image features, facilitating the extraction and suppression of the image background. Subsequently, through self-attention operations on target confidence maps and feature maps, fusion of shallow and deep features in images is achieved. This accomplishes pixel-level segmentation of infrared small targets, fulfilling the purpose of target detection. Experiments demonstrate in infrared sequence image dim and small aircraft target detection and tracking data set, even when applied to infrared images with complex background and noisy, our method outperforms the state-of-the-art detection methods. The method shows good robustness and high detection accuracy. When the threshold is selected to maximize the average of FM, the detection rate of our method reaches 0.997 2, its false alarm rate is 2.82×10-7, the precision rate is 0.912 7, and the recall rate is 0.921.