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

基于注意力的多通道网络红外弱小目标检测

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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.

摘要

红外探测技术具有全天候检测、隐蔽性好的优点, 被广泛应用在远距离目标探测与跟踪系统 中, 而复杂的背景、**烈的噪音以及目标尺寸小、**度弱的特性给红外弱小目标检测任务带来了很 大的困难。针对传统算法漏检率与虚警率高以及深度学**算法模型大、复杂度高、检测性能有待改 善的问题, 我们提出了基于注意力的多通道网络检测算法。首先, 考虑到多尺度红外弱小目标的特 征提取困难, 我们设计了基于膨胀卷积的多通道网络来捕获多尺度的目标特征。其次, 通过在各通 道中嵌入坐标注意力模块自适应地抑制背景杂波并增**目标特征。此外, 通过上下文注意力结构实 现浅层细节特征和深层抽象语义特征的融合。最后, 基于SIRST和MDFA数据集与各先进检测算法进 行对比, 验证了本文提出的算法能够进一步改善检测性能, 同时模型的参数量和计算复杂度更小。

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Authors and Affiliations

  1. Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yanjun Zhang  (张彦军) & Yunze Cai  (蔡云泽)

  2. Shanghai Institute of Aerospace Control Technology, Shanghai, 201109, China

    Biyun Wang  (王碧云)

  3. Infrared Detection Technology R&D Center of China Aerospace Science and Technology Corporation, Shanghai, 201109, China

    Biyun Wang  (王碧云)

  4. Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China

    Yanjun Zhang  (张彦军) & Yunze Cai  (蔡云泽)

  5. Shanghai Engineering Research Center of Intelligent Control and Management, Shanghai, 200240, China

    Yanjun Zhang  (张彦军) & Yunze Cai  (蔡云泽)

  6. Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yanjun Zhang  (张彦军) & Yunze Cai  (蔡云泽)

  7. Institute of Marine Equipment, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yanjun Zhang  (张彦军) & Yunze Cai  (蔡云泽)

Authors
  1. Yanjun Zhang  (张彦军)
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  2. Biyun Wang  (王碧云)
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  3. Yunze Cai  (蔡云泽)
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Corresponding author

Correspondence to Yunze Cai  (蔡云泽).

Additional information

Foundation item: the Industry-University-Research Cooperation Fund Project of the Eighth Research Institute of China Aerospace Science and Technology Corporation (No. USCAST2021-5), the Major Scientific Instrument Research of National Natural Science Foundation of China (No. 61627810), the National Science and Technology Major Program of China (No. 2018YFB1305003), and the National Defense Science and Technology Outstanding Youth Science Foundation (No. 2017-JCJQ-ZQ-031)

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Zhang, Y., Wang, B. & Cai, Y. Multi-Channel Based on Attention Network for Infrared Small Target Detection. J. Shanghai Jiaotong Univ. (Sci.) 29, 414–427 (2024). https://doi.org/10.1007/s12204-023-2616-9

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

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