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2025 , Vol. 30 >Issue 5: 935 - 951

DOI: https://doi.org/10.1007/s12204-024-2694-3

Computing & Computer Technologies

MAGPNet: Multi-Domain Attention-Guided Pyramid Network for Infrared Small Object Detection

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  • 1. Department of Automation, Shanghai Jiao Tong University; Key Laboratory of System Control and Information Processing of Ministry of Education, Shanghai 200240, China; 2. National Key Laboratory of Air-based Information Perception and Fusion, Luoyang 471009, Henan, China; 3. Shanghai Institute of Aerospace Control Technology; Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation, Shanghai 201109, China

Received date: 2023-03-21

  Accepted date: 2023-07-14

  Online published: 2024-01-05

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Abstract

To overcome the obstacles of poor feature extraction and little prior information on the appearance of infrared dim small targets, we propose a multi-domain attention-guided pyramid network (MAGPNet). Specifically, we design three modules to ensure that salient features of small targets can be acquired and retained in the multi-scale feature maps. To improve the adaptability of the network for targets of different sizes, we design a kernel aggregation attention block with a receptive field attention branch and weight the feature maps under different perceptual fields with attention mechanism. Based on the research on human vision system, we further propose an adaptive local contrast measure module to enhance the local features of infrared small targets. With this parameterized component, we can implement the information aggregation of multi-scale contrast saliency maps. Finally, to fully utilize the information within spatial and channel domains in feature maps of different scales, we propose the mixed spatial-channel attention-guided fusion module to achieve high-quality fusion effects while ensuring that the small target features can be preserved at deep layers. Experiments on public datasets demonstrate that our MAGPNet can achieve a better performance over other state-of-the-art methods in terms of the intersection of union, Precision, Recall, and F-measure. In addition, we conduct detailed ablation studies to verify the effectiveness of each component in our network.

Cite this article

DING Leqi, WANG Biyun, YAO Lixiu, CAI Yunze . MAGPNet: Multi-Domain Attention-Guided Pyramid Network for Infrared Small Object Detection[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(5) : 935 -951 . DOI: 10.1007/s12204-024-2694-3

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