Edge-Guided Perceptual Network for Infrared Small Target Detection

Infrared small target detection (IRSTD) plays a critical role in applications such as night navigation and fire rescue. Its primary purpose is to extract small targets from cluttered backgrounds. While deep learning-based methods have made great advancements in this field, there are still some limitations. One common issue is that the detected target shape tends to be smooth, and extremely small targets may not be effectively detected due to background interference. This article proposes an edge-guided perception network (EGPNet) for IRSTD to alleviate this trouble. To maintain the information of small targets, EGPNet utilizes a multiscale feature progressive fusion (MFPF) encoder to extract features. This progressive fusion manner enhances semantic information and contextual correlation. Considering that the detected target shapes may result in smoothing effect, an edge-guided image refinement module (EIRM) is incorporated to improve the integrity of the target shape. Moreover, we introduce a local target amplifier (LTA) to boost the visibility and representation of targets, while suppressing the clutter background interference. The experimental results illustrate that the proposed model can detect the targets with small and weak in different scenes well. Our code is publicly available at https://github.com/qianngli/EGPNet.