Target-Focused Enhancement Network for Distant Infrared Dim and Small Target Detection

Abstract

In the context of long-range infrared detection of small targets, complex battlefield environments with strong background effects, adverse weather conditions, and intense light interference pose significant challenges. These factors contribute to a low signal-to-noise ratio and limited target information in infrared imagery. To address these challenges, a feature enhancement network called target-focused enhancement network (TENet) is proposed with two key innovations: the dense long-distance constraint (DLDC) module and the autoaugmented copy-paste bounding-box (ACB) strategy. The DLDC module incorporates a self-attention mechanism to provide the model with a global understanding of the relationship between small targets and the backgrounds. By integrating a multiscale structure and using dense connections, this module effectively transfers global information to the deep layers, thus enhancing the semantic features. On the other hand, the ACB strategy focuses on data enhancement, particularly increasing the target representation. This approach addresses the challenge of distributional bias between small targets and background using context information for target segmentation, mapping augment strategies to 2-D space, and using an adaptive paste method to fuse the target with the background. The DLDC module and the ACB strategy complement each other in terms of features and data, leading to a significant improvement in model performance. Experiments on infrared datasets with complex backgrounds demonstrate that the proposed network achieves superior performance in detecting dim and small targets.