IRSTFormer: A Hierarchical Vision Transformer for Infrared Small Target Detection

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2022-07, Vol.14 (14), p.3258
• Main Authors: Chen, Gao, Wang, Weihua, Tan, Sirui
• Format: Article
• Language: English
• Subjects: Ablation; Adaptability; Artificial neural networks; Datasets; deep learning; Design; False alarms; Field of view; Infrared imagery; infrared small target detection; Infrared tracking; Methods; Neural networks; Optimization; Remote sensing; self-attention; Sensors; Target detection; transformer
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14143258

Infrared small target detection occupies an important position in the infrared search and track system. The most common size of infrared images has developed to 640×512. The field-of-view (FOV) also increases significantly. As the result, there is more interference that hinders the detection of small targets in the image. However, the traditional model-driven methods do not have the capability of feature learning, resulting in poor adaptability to various scenes. Owing to the locality of convolution kernels, recent convolutional neural networks (CNN) cannot model the long-range dependency in the image to suppress false alarms. In this paper, we propose a hierarchical vision transformer-based method for infrared small target detection in larger size and FOV images of 640×512. Specifically, we design a hierarchical overlapped small patch transformer (HOSPT), instead of the CNN, to encode multi-scale features from the single-frame image. For the decoder, a top-down feature aggregation module (TFAM) is adopted to fuse features from adjacent scales. Furthermore, after analyzing existing loss functions, a simple yet effective combination is exploited to optimize the network convergence. Compared to other state-of-the-art methods, the normalized intersection-over-union (nIoU) on our IRST640 dataset and public SIRST dataset reaches 0.856 and 0.758. The detailed ablation experiments are conducted to validate the effectiveness and reasonability of each component in the method.