Abstract: The task of infrared dim and small target detection is one of the key research contents in the field of infrared detection.However,due to the particularity of its application scenarios,the data containing infrared dim and small targets is rare,and often not fully labeled,which poses challenges and difficulties for data-driven deep learning object detection models.In order to solve the problems of limited datasets and lack of label information,an infrared dim and small target detection model based on cross-domain migration of visible light and infrared is proposed to migrate the more abundant visible light domain supervision information to the infrared domain,so as to achieve unsupervised training in the infrared domain.First,a channel augmentation data proces-sing method is designed on the basis of YOLOv5,utilizing low-cost channel separation techniques to convert visible light images into infrared like images,reducing the modal differences between the visible and infrared domains.Then,a multi-scale domain adaptive module is constructed,and the features of different scales extracted by the backbone network are used in the way of adversarial training.Domain confusion is performed in the feature space to reduce the impact of domain shift and improve the detection performance of dim and small target detection.Experimental results show that the improved model by the proposed method can improve the average detection precision compared to various versions of the YOLOv5 original model.Compared with other existing unsupervised domain adaptive target detection algorithms,the proposed method is obviously superior in the detection accuracy of small infrared targets.

Key words: Infrared dim and small targets, Object detection, Deep learning, Domain adaptive, Unsupervised