基于自适应对比度增强的红外小目标检测网络
作者:
作者单位:

1.上海科技大学 信息科学与技术学院,上海 201210;2.中国科学院上海技术物理研究所 红外探测与成像技术重点实验室,上海 200083

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中图分类号:

TP391.4

基金项目:


ACE-STDN: An infrared small target detection network with adaptive contrast enhancement
Author:
Affiliation:

1.School of Information Science and Technology, ShanghaiTech University, Shanghai 201210, China;2.Key Laboratory of Infrared System Detection and Imaging Technology, Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China

Fund Project:

Supported by the Youth Innovation Promotion Association CAS (2014216); Supported by the National Pre-research Program during the 14th Five-Year Plan (514010405).

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    摘要:

    红外探测系统需要尽早发现目标以便及时拦截,但是红外图像上的小目标检测是一个挑战十足的任务。为了提高检测准确率,提出一种基于自适应对比度增强的红外小目标检测方法。为了利用自注意力机制和卷积各自的优势,设计了一个高效的特征提取网络和一个面向小目标的检测头。同时为了解决实际应用中出现的弱目标,在检测子网络前添加了一个图像预处理子网络,该模块可以自适应地调节图像对比度。在红外空中小目标数据集上的实验表明,提出的方法能达到93.76%的检测精度,与经典的检测方法相比,能够更好地平衡检测精度和召回率,证明了方法的巨大应用潜力。

    Abstract:

    Due to the long distance and complex background, it is hard for the infrared detecting and tracking system to find and locate the dim-small targets in time. The proposed method, ACE-STDN, aims to tackle this difficult task and improve the detection accuracy. First of all, an adaptive contrast enhancement subnetwork preprocesses the input infrared image, which is conducive for the low-contrast dim targets. Next, a detection subnetwork with a hybrid backbone takes advantage of both convolution and self-attention mechanisms. Besides, the regression loss is designed based on 2D Gaussian distribution representation instead of Intersection over Union measurement. To verify the effectiveness and efficiency of our method, we conduct extensive experiments on two public infrared small target datasets. The experimental results show that the model trained by our method has a significant improvement in detection accuracy compared with other traditional and data-based algorithms, with the average precision reaching 93.76%. In addition, ACE-STDN achieves outstanding detection performance in a multiclass object dataset and a general small object dataset, verifying the effectiveness and robustness.

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引用本文

叶昕怡,高思莉,李范鸣.基于自适应对比度增强的红外小目标检测网络[J].红外与毫米波学报,2023,42(5):701~710]. YE Xin-Yi, GAO Si-Li, Li Fan-Ming. ACE-STDN: An infrared small target detection network with adaptive contrast enhancement[J]. J. Infrared Millim. Waves,2023,42(5):701~710.]

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  • 收稿日期:2022-12-19
  • 最后修改日期:2023-08-14
  • 录用日期:2023-02-02
  • 在线发布日期: 2023-08-06
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