BAUENet: Boundary-Aware Uncertainty Enhanced Network for Infrared Small Target Detection
Tianxiang Chen, Qi Chu, Zhentao Tan, Bin Liu, Nenghai Yu
Abstract
Infrared small target detection (ISTD) is indispensable in remote sensing and military surveillance. Existing ISTD methods can discover regularly-shaped and clear objects well, but tend to overlook the tough-to-detect ones, such as targets with irregular shapes or blurry boundaries, causing inaccurate segmentation and missed detection. Considering that boundary areas assemble rich uncertainty information, we propose the Boundary-Aware Uncertainty Enhanced Network (BAUENet), where Uncertainty Enhanced Context Refinement (UECR) and Adaptive Feature Fusion Modules (AFFM) are devised to address this problem. Specifically, UECR extracts spatial contexts and refines them with uncertain area maps derived from backbone intermediate outputs, so as to distinguish boundary areas from other regions. AFFM adaptively aggregates cross-level features via balancing low-level details and high-level semantics for finer boundary preservation in both channel and spatial dimensions during up-sampling feature fusion. Experiments on several public datasets demonstrate the effectiveness of the proposed method, especially for irregular shape and blurry boundary cases.