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Journal of Shanghai Jiaotong University(Science) >

2024 , Vol. 29 >Issue 3: 414 - 427

DOI: https://doi.org/10.1007/s12204-023-2616-9

Automation & Computer Technologies

Multi-Channel Based on Attention Network for Infrared Small Target Detection

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  • (1. Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Shanghai Institute of Aerospace Control Technology, Shanghai 201109, China; 3. Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation, Shanghai 201109, China; 4. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240, China; 5. Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai 200240, China; 6. Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China; 7. Institute of Marine Equipment, Shanghai Jiao Tong University, Shanghai 200240, China)

Accepted date: 2022-04-23

  Online published: 2024-05-28

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Abstract

Infrared detection technology has the advantages of all-weather detection and good concealment,which is widely used in long-distance target detection and tracking systems. However, the complex background,the strong noise, and the characteristics of small scale and weak intensity of targets bring great difficulties to the detection of infrared small targets. A multi-channel based on attention network is proposed in this paper, aimed at the problem of high missed detection rate and false alarm rate of traditional algorithms and the problem of large model, high complexity and poor detection performance of deep learning algorithms. First, given the difficulty in extracting the features of infrared multiscale and small dim targets, the multiple channels are designed based on dilated convolution to capture multiscale target features. Second, the coordinate attention block is incorporated in each channel to suppress background clutters adaptively and enhance target features. In addition, the fusion of shallow detail features and deep abstract semantic features is realized by synthesizing the contextual attention fusion block. Finally, it is verified that, compared with other state-of-the-art methods based on the datasets SIRST and MDFA, the proposed algorithm further improves the detection effect, and the model size and computational complexity are smaller.

Key words： infrared image, small target detection, deep learning, attention mechanism, feature fusion

Cite this article

ZHANG Yanjun(张彦军), WANG Biyun(王碧云)，CAI Yunze (蔡云泽) . Multi-Channel Based on Attention Network for Infrared Small Target Detection[J]. Journal of Shanghai Jiaotong University(Science), 2024 , 29(3) : 414 -427 . DOI: 10.1007/s12204-023-2616-9

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