平滑交互式压缩网络的红外小目标检测算法

张铭津; 周楠; 李云松

doi:10.19665/j.issn1001-2400.20231203

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平滑交互式压缩网络的红外小目标检测算法

信息与通信工程 | 更新时间：2024-11-13

- 平滑交互式压缩网络的红外小目标检测算法
- Smooth interactive compression network for infrared small target detection
- 西安电子科技大学学报 2024年51卷第4期页码：1-14
- 作者机构：
  
  西安电子科技大学通信工程学院,陕西西安 710071
- 作者简介：
  
  [ "张铭津(1988—),女,教授,E-mail:[email protected]" ]
  [ "周楠(2002—),女,西安电子科技大学本科生,E-mail:[email protected]" ]
  [ "李云松(1974—),男,教授,E-mail:[email protected]" ]
- 基金信息：
  
  国家自然科学基金(62272363);国家自然科学基金(62036007);中国科协青年人才托举工程(2021QNRC001);星载计算机与电子技术创新联合实验室2023年度开放基金(2024KFKT001-1)
- DOI：10.19665/j.issn1001-2400.20231203
  中图分类号： TN219;TN215;TN919.81
- 纸质出版日期：2024-08-20，
  
  网络出版日期：2023-12-27，
  
  收稿日期：2023-07-17，
- 稿件说明：
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张铭津, 周楠, 李云松. 平滑交互式压缩网络的红外小目标检测算法[J]. 西安电子科技大学学报, 2024,51(4):1-14.

Mingjin ZHANG, Nan ZHOU, Yunsong LI. Smooth interactive compression network for infrared small target detection. [J]. Journal of Xidian University, 2024,51(4):1-14.
张铭津, 周楠, 李云松. 平滑交互式压缩网络的红外小目标检测算法[J]. 西安电子科技大学学报, 2024,51(4):1-14. DOI： 10.19665/j.issn1001-2400.20231203.

Mingjin ZHANG, Nan ZHOU, Yunsong LI. Smooth interactive compression network for infrared small target detection. [J]. Journal of Xidian University, 2024,51(4):1-14. DOI： 10.19665/j.issn1001-2400.20231203.

摘要

红外小目标检测是对地观测、抢险救灾等诸多领域的重要课题

一直受到学界的广泛关注。由于红外小目标通常只占据几十个像素且分布在整个背景中

因此大范围内探索图像特征之间的语义信息以挖掘目标与背景之间的差异对检测性能的提升至关重要。然而

传统卷积神经网络的编码局域性与计算资源的巨大需求削弱了网络捕获小目标形状和位置的能力

极易产生漏检与虚警。基于此

提出了一种平滑交互式压缩网络模型

主要包含平滑交互模块与交叉关注模块。平滑交互模块在拓展特征图感受野的同时增添其依赖性

提升了网络在复杂背景条件下检测性能的鲁棒性。交叉关注模块综合考量信道的贡献度与剪枝的可解释性

从而动态融合不同分辨率的特征图。最后

在公开的SIRST数据集和IRSTD-1K数据集上的大量试验结果表明

提出的网络可以有效地解决目标丢失、虚警率高、视觉效果不佳等问题。以SIRST数据集为例

与性能第2的模型相比

IoU、nIoU和

分别提高了约3.05%、3.41%和1.02%;

和FLOPs分别降低了约33.33%和82.30%。

Abstract

Infrared Small Target Detection is a critical focus of various fields

including earth observation and disaster relief efforts

receiving considerable attention within the academic community.Since infrared small targets often occupy just a few dozen pixels and are scattered within complex backgrounds

it becomes paramount to extract semantic information from a broad range of image features to distinguish targets from their surroundings and enhance detection performance.Traditional convolutional neural networks

due to their limited receptive fields and substantial computational demands

face challenges in effectively capturing the shape and precise positioning of small targets

leading to missed detections and false alarms.In response to these challenges

this paper proposes a novel Smooth Interactive Compression Network comprising two main components:the Smooth Interaction Module and the Cross Compression Module.The Smooth Interaction Module extends the feature map's receptive field and enhances inter-feature dependencies

thus bolstering the network’s detection robustness in complex background scenarios.The Cross Compression Module takes into account channel contributions and the interpretability of pruning

dynamically fusing feature maps of varying resolutions.Extensive experiments conducted on the publicly available SIRST dataset and IRSTD-1K dataset demonstrate that the proposed network effectively addresses issues such as target loss

a high false alarm rate

and subpar visual results.Taking the SIRST dataset as an example

compared to the second-best performing model

the proposed model achieved a remarkable improvement in metrics:IoU

nIoU

and

are increased by 3.05%

3.41%

and 1.02%

respectively.Meanwhile

and FLOPs are decreased by 33.33% and 82.30%

respectively.

关键词

红外小目标检测深度学习网络编码模型压缩

Keywords

Infrared Small Target Detectiondeep learningnetwork codingmodel compression

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