Pay Attention to Local Contrast Learning Networks for Infrared Small Target Detection

Infrared small target suffers from the lack of intrinsic features, context, and samples. Conventional detection methods are usually unable to sufficiently and effectively extract the features of infrared small targets. Therefore, we propose a novel attention-based local contrast learning network (ALCL-Net). Considering the scarcity of intrinsic features of infrared small targets, we propose ResNet32, which enhances the ability to extract infrared small target features and avoids the problem that the target features are overwhelmed by the background features due to too deep network. At the same time, we construct a simplified bilinear interpolation attention module (SBAM), which is used for fusion of hierarchical feature maps. It has fast inference speed and can focus on the feature of the target in the lack of context. Furthermore, local contrast learning (LCL) is introduced, which adopts the local contrast idea of nondeep learning methods. It can alleviate the dependence on dataset samples, thereby improving detection accuracy on datasets with few samples. Compared with the state-of-the-art methods, the proposed ALCL-Net achieves superior performance with an intersection over union (IoU) of 0.792 and a normalized IoU (nIoU) of 0.771 on the public single-frame infrared small target (SIRST) dataset.