Gradient-Guided Learning Network for Infrared Small Target Detection

TL;DR

This paper introduces GGL-Net, a novel deep learning framework that leverages gradient magnitude images and dual-branch feature extraction to improve infrared small target detection accuracy, especially in challenging backgrounds.

Contribution

It is the first to incorporate gradient magnitude images into deep learning for infrared small target detection, enhancing edge detail and detection precision.

Findings

Achieves state-of-the-art results on NUAA-SIRST dataset.

Effectively emphasizes edge details of small targets.

Improves fusion of multi-scale features for better detection.

Abstract

Recently, infrared small target detection has attracted extensive attention. However, due to the small size and the lack of intrinsic features of infrared small targets, the existing methods generally have the problem of inaccurate edge positioning and the target is easily submerged by the background. Therefore, we propose an innovative gradient-guided learning network (GGL-Net). Specifically, we are the first to explore the introduction of gradient magnitude images into the deep learning-based infrared small target detection method, which is conducive to emphasizing the edge details and alleviating the problem of inaccurate edge positioning of small targets. On this basis, we propose a novel dual-branch feature extraction network that utilizes the proposed gradient supplementary module (GSM) to encode raw gradient information into deeper network layers and embeds attention mechanisms reasonably to enhance feature extraction ability. In addition, we construct a two-way guidance fusion module (TGFM), which fully considers the characteristics of feature maps at different levels. It can facilitate the effective fusion of multi-scale feature maps and extract richer semantic information and detailed information through reasonable two-way guidance. Extensive experiments prove that GGL-Net has achieves state-of-the-art results on the public real NUAA-SIRST dataset and the public synthetic NUDT-SIRST dataset. Our code has been integrated into https://github.com/YuChuang1205/MSDA-Net