BPR-Net: Balancing Precision and Recall for Infrared Small Target Detection

Most current infrared small target detection methods attempt to fuse local and global information by using single-scale inputs and creating a multi-scale feature pyramid during network feeding forwards. Further to this, our research finds that using high-resolution inputs can improve recall, while low-resolution inputs improve precision. Nevertheless, solely focusing on global or local information can result in missing target and false alarm. To address these issues, we propose the BPR-Net to balance precision and recall via a novel multi-scale attention mechanism, which combines semantic and shallow features of multi-scale inputs. We first scale the input image into multiple images with varying resolutions and feed them into the network. In the encoder, Scale Fusion Module (SFM) fuses features from corresponding images of different resolutions. In the decoder, a Channel Fusion Module (CFM) fuses useful information from multiple channels. Furthermore, a Wavelet Transform cross-layer skip Layer (WTL) is employed to enhance the interaction between decoder layers for more effective multi-scale feature fusion. Experimental results demonstrate that our approach achieves a balance between recall and precision and yields state-of-the-art performance on challenging benchmarks including Sirst, MDvsFA, and SIATD. Notably, our approach achieves an F1 score of 0.9409 on the challenging benchmark SIATD, surpassing the state-of-the-art method by 16.7%.