Multilevel Interactive Enhanced Network for Infrared Small-Target Detection

Youliang Chu, Ming Cheng, Zhiyang Lu, Zhentao Xiong, Cheng Wang

Abstract: Infrared small-target detection (IRSTD) aims to identify small and faint targets amidst cluttered background in infrared images, which is vital for applications, such as maritime surveillance. Traditional methods struggle due to low signal-to-noise ratio (SNR) and contrast. However, recent CNN-based approaches show promise, leveraging deep learning’s strong modeling capabilities. In this letter, we propose a multilevel interactive enhanced network (MIE-Net). In MIE-Net, we use multiple backbones that have progressively decreasing numbers of blocks. Features transfer and information interaction are carried out between different backbones. We designed an attention mechanism-based feature filter (AFF) to reduce background noise interference by filtering the low-level features with high-level features. Furthermore, we proposed a global information enhancement module (GIEM), through which features are enhanced as they are delivered, while further mitigating the problem of small-target loss. Experiments on public datasets validate the effectiveness of our method. MIE-Net outperforms the current state-of-the-art (SOTA) methods by approximately 6% in terms of the intersection over union (IoU). There was also about a 2% increase in average area under the curve (AUC).