伪装目标检测研究进展
Research developments in camouflage object detection
张冬冬 1王春平 1付强1
作者信息
- 1. 陆军工程大学石家庄校区电子与光学工程系,石家庄 050003
- 折叠
摘要
伪装目标检测旨在准确地检测出"完美"隐藏在周围环境中的目标,是一项重要且极具挑战性的任务.当前,伪装目标检测在计算机视觉领域引发了广泛关注,并且学者们已成功构建了多种类型的检测模型.但是,大多数工作以构建高效的检测模型为目的,缺少对已有模型的深入分析及归纳总结.因此,对现有伪装目标检测模型进行了全面分析和总结,并探讨了伪装目标检测潜在的研究方向.首先从传统方法和基于深度学习方法两个大类对已存的模型进行全面综述,并详细阐述相关模型的原理和优劣势;其次介绍了伪装目标检测领域常用数据集及评价指标;最后总结全文并对伪装目标检测领域的未来研究方向进行了展望.
Abstract
Camouflaged object detection is an important and challenging task which aims to accurately detect tar-gets which are"perfectly"hidden in the surrounding environment.Currently,camouflaged object detection has attrac-ted widespread attention in the field of computer vision,and scholars have successfully proposed various types of detec-tion models.However,most of the work is aimed at building efficient detection models,and there is a lack of in-depth analysis and generalization of existing models.Therefore,this paper presents a comprehensive analysis and summary of existing camouflaged object detection models and discusses potential research directions for camouflaged object detec-tion.Firstly,an overall review of the existing models is given in two broad categories,traditional methods and deep learning-based methods,and the principles,advantages and disadvantages of the relevant models are elaborated;Sec-ondly,common datasets and evaluation metrics in the field of camouflaged object detection are introduced;Then,ex-isting deep learning-based camouflaged object detection models are reproduced,and the detection results of different types of models on public datasets are compared in both qualitative and quantitative terms;Finally,the whole paper is summarized and future research directions in the field of camouflaged object detection are prospected.
关键词
伪装目标检测/传统方法/深度学习Key words
camouflaged object detection/traditional method/deep learning引用本文复制引用
基金项目
军内某科研项目(LJ20191A040155)
出版年
2024
NETL
NSTL
万方数据