A Saliency-Transformer Combined Knowledge Distillation Guided Network for Infrared Small Target Detection

Abstract

AbstractInfrared small target (IRST) detection focuses on segmenting small infrared targets from complex backgrounds. Recent Convolutional Neural Networks (CNNs) show strong performance on detecting infrared small targets with complex background. Existing CNNs-based methods mainly have two weaknesses. First, features of small targets are likely to lose in deep stages of networks. Second, infrared small targets are always shapeless, which will cause more false detections. To solve the above mentioned two weaknesses, we propose a saliency-transformer combined knowledge distillation guided network (ST-KDNet). In our proposed ST-KDNet, we first use transformer-based segmentation branch to extract the attention region of small targets. Then we apply saliency detection branch to filter some irrelevant similar targets, where the saliency mask is used to guide the transformer-based segmentation branch. To further enhance representation ability of small target on the low-level feature, we introduce a knowledge distillation guidance. Extensive experiments on benchmark datasets, MDFA and SIRST, prove that ST-KDNet outperforms previous state-of-the-art (SOTA) methods.KeywordsST-KDNetSaliency-transformerKnowledge distillationInfrared small target detection