基于注意力的多通道网络红外弱小目标检测

doi:10.1007/s12204-023-2616-9

J Shanghai Jiaotong Univ Sci ›› 2024, Vol. 29 ›› Issue (3): 414-427.doi: 10.1007/s12204-023-2616-9

基于注意力的多通道网络红外弱小目标检测

张彦军1,4,5,6,7, 王碧云2,3 , 蔡云泽1,4,5,6,7

(1.上海交通大学自动化系，上海 200240； 2.上海航天控制技术研究所，上海 201109； 3. 中国航天科技集团红外探测技术研发中心，上海 201109； 4. 系统控制与信息处理教育部重点实验室，上海 200240； 5. 上海工业智能管控工程技术研究中心，上海 200240； 6. 上海交通大学海洋智能装备与系统集成技术教育部实验室，上海 200240； 7. 上海交通大学海洋装备研究院，上海 200240)

接受日期:2022-04-23 出版日期:2024-05-28 发布日期:2024-05-28

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

ZHANG Yanjun^1,4,5,6,7 (张彦军), WANG Biyun^2,3 (王碧云)，CAI Yunze^1,4,5,6,7∗ (蔡云泽)

(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:2022-04-23 Online:2024-05-28 Published:2024-05-28

摘要/Abstract

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、强烈的噪音以及目标尺寸小、强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习算法模型大、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强目标特征。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。

关键词: 红外图像，弱小目标检测，深度学习，注意力机制，特征融合

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

中图分类号:

TP183

张彦军1,4,5,6,7, 王碧云2,3 , 蔡云泽1,4,5,6,7. 基于注意力的多通道网络红外弱小目标检测[J]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 414-427.

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]. J Shanghai Jiaotong Univ Sci, 2024, 29(3): 414-427.

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基于注意力的多通道网络红外弱小目标检测

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

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