Guided Attention and Joint Loss for Infrared Dim Small Target Detection

Abstract

Infrared dim small target (IDST) detection is of great significance in security surveillance and disaster relief. However, the complex background interference and tiny targets in infrared images keep it still a long-term challenge. Existing deep learning models stack network layers to expand the model fitting capability, but this operation also increases redundant features which reduce model speed and accuracy. Meanwhile, small targets are more susceptible to positional bias, with this dramatically reducing the model’s localization accuracy. In this article, we propose a guided attention and joint loss (GA-JL) network for infrared small target detection. More specifically, the method visualizes the feature maps at each resolution through a two-branch detection head (TDH) module, filters out the features that are strongly related to the task, and cuts out the redundant features. On this basis, the guided attention (GA) module guides the prediction layer features using the features that are associated closely with the task and combines spatial and channel bidirectional attention to make the prediction layer feature maps embedded with effective messages. Finally, through the joint loss (JL) module, the target position regression is performed with multiangle metrics for enhancing the target detection accuracy. Experimental results of our method on the SIATD, SIRST, and IRSTD_1k datasets reveal that it is capable of accurately identifying IDSTs, remarkably reduces the false alarm rate, and outperforms other methods.