Multi-Scale Direction-Aware Network for Infrared Small Target Detection

TL;DR

This paper introduces MSDA-Net, a novel neural network that effectively detects infrared small targets by integrating high-frequency directional features and multi-scale local relations, achieving state-of-the-art results.

Contribution

The paper proposes MSDA-Net, the first network to incorporate high-frequency directional features as domain prior knowledge for infrared small target detection.

Findings

Achieves SOTA performance on multiple datasets.

Effectively extracts high-frequency directional features.

Improves target detection accuracy in infrared images.

Abstract

Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. Existing deep learning-based methods focus on edge and shape features, but ignore the richer structural differences and detailed information embedded in high-frequency components from different directions, thereby failing to fully exploit the value of high-frequency directional features in target perception. To address this limitation, we propose a multi-scale direction-aware network (MSDA-Net), which is the first attempt to integrate the high-frequency directional features of infrared small targets as domain prior knowledge into neural networks. Specifically, to fully mine the high-frequency directional features, on the one hand, a high-frequency direction injection (HFDI) module without trainable parameters is constructed to inject the high-frequency directional information of the original image into the network. On the other hand, a multi-scale direction-aware (MSDA) module is constructed, which promotes the full extraction of local relations at different scales and the full perception of key features in different directions. In addition, considering the characteristics of infrared small targets, we construct a feature aggregation (FA) structure to address target disappearance in high-level feature maps, and a feature calibration fusion (FCF) module to alleviate feature bias during cross-layer feature fusion. Extensive experimental results show that our MSDA-Net achieves state-of-the-art (SOTA) results on multiple public datasets. The code can be available at https://github.com/YuChuang1205/MSDA-Net