单点监督下红外小目标检测的混合掩模生成

IF 6.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Weijie He, Mushui Liu, Yunlong Yu
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引用次数: 0

摘要

单帧红外小目标检测由于需要在复杂的红外背景杂波中识别微小目标,给单帧红外小目标检测带来了很大的挑战。在本文中,我们将一种新的无学习方法与基于学习方法的混合方法相结合,研究了一种弱监督范式,从点级标注中获得高质量的伪掩码。无学习方法遵循顺序过程,从点标注到包含目标的边界框,再到详细的伪掩码,而混合则通过过滤掉网络预测中的虚警和检索漏检来实现,为无学习掩码提供可靠的补充。实验结果表明,我们的无学习方法生成的伪掩码在三个数据集上的平均IoU比第二好的无学习方法高4.3%,而基于混合学习的方法进一步提高了伪掩码的质量,平均IoU增加了3.4%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hybrid mask generation for infrared small target detection with single-point supervision
Single-frame infrared small target (SIRST) detection poses a significant challenge due to the requirement to discern minute targets amidst the complex infrared background clutter. In this paper, we focus on a weakly-supervised paradigm to obtain high-quality pseudo masks from the point-level annotation by integrating a novel learning-free method with the hybrid of the learning-based method. The learning-free method adheres to a sequential process, progressing from a point annotation to the bounding box that encompasses the target, and subsequently to detailed pseudo masks, while the hybrid is achieved through filtering out false alarms and retrieving missed detections in the network’s prediction to provide a reliable supplement for learning-free masks. The experimental results show that our learning-free method generates pseudo masks with an average Intersection over Union (IoU) that is 4.3 % higher than the second-best learning-free competitor across three datasets, while the hybrid learning-based method further enhances the quality of pseudo masks, achieving an additional average IoU increase of 3.4 %.
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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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