结合跨尺度特征融合与瓶颈注意力模块的轻量型红外小目标检测网络
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作者单位:

国防科技大学 电子科学学院,湖南 长沙 410073

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中图分类号:

TP753

基金项目:

国家自然科学基金(61972435, 61401474, 61921001, 62001478)


Light-weight infrared small target detection combining cross-scale feature fusion with bottleneck attention module
Author:
Affiliation:

College of electronic science and technology, National University of Defense Technology, Changsha 410073, China

Fund Project:

Supported by National Natural Science Foundation of China (61972435, 61401474, 61921001, 62001478)

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    摘要:

    提出一种结合跨尺度特征融合与瓶颈注意力模块的轻量型单帧红外小目标检测网络。该网络在不引入额外神经元的前提下,直接在编码层和解码层之间进行高频多尺度特征交互,从而维持小目标在网络深层的响应幅值,实现小目标浅层空间结构特征与深层高级语义特征之间的交互融合。同时,该网络在编码器瓶颈处级联轻量型混合注意力模块,进一步增强目标特征在网络深层的响应幅值。实验结果表明,该网络能有效抑制复杂背景杂波,并以较低参数量实现红外小目标检测。

    Abstract:

    This paper proposed a light-weight single frame infrared small target detection network that combined cross-scale feature fusion and bottleneck attention module. Instead of bringing extra huge neurons, the network directly performs cross-scale feature interaction between the encoding and decoding sub-networks, maintain the response of small target in the deep CNN layers, and thus achieves the full fusion between the spatial structure features from shallow layers and high-level semantic features from deep layers. Based on cross-scale feature fusion module, a light-weight bottleneck attention module is introduced to further enhance the response the target feature in the deep layers of the network. Experimental results demonstrate that the network can effectively suppress the complex background clutter and achieve high performance of infrared small target detection with low amount of parameters.

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林再平,李博扬,李淼,王龙光,吴天昊,罗伊杭,肖超,李若敬,安玮.结合跨尺度特征融合与瓶颈注意力模块的轻量型红外小目标检测网络[J].红外与毫米波学报,2022,41(6):1102~1112]. LIN Zai-Ping, LI Bo-Yang, LI Miao, WANG Long-Guang, WU Tian-Hao, LUO Yi-Hang, XIAO Chao, LI Ruo-Jing, An Wei. Light-weight infrared small target detection combining cross-scale feature fusion with bottleneck attention module[J]. J. Infrared Millim. Waves,2022,41(6):1102~1112.]

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  • 收稿日期:2022-06-13
  • 最后修改日期:2022-11-17
  • 录用日期:2022-08-08
  • 在线发布日期: 2022-11-15
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