摘要
伪装检测作为一个新兴研究方向,其目标是将隐藏在图像背景中的目标对象快速准确地检测出来,可应用于物种保护和军事监测等领域,具有较高的应用价值。介绍伪装目标检测的基本概念,并按照伪装检测算法所采用的关键技术进行分类,具体从传统的伪装检测方法和基于深度学习的伪装检测方法两大类进行论述,详细地分析相关方法的原理和技术细节。并且,介绍伪装目标检测与分割相关数据集和性能评价指标,并对代表性算法进行实验对比和性能分析,得出基于深度学习的伪装检测方法效果更好。
Camouflage object detection(COD)is a new research field,its goal is to detect those objects hidden in the scene image background,which can be applied to species protection,military monitoring and etc,has a wide range of application area.Firstly,the basic concept of camouflage detection is introduced.According to the key technologies adopted,COD methods can be divided into two categories:traditional camouflage detection methods based on hand-crafted features,and deep learning based camouflage detection methods.Both the categories are analyzed and discussed in detail,including the principle and technical details.Secondly,the corresponding data sets and performance metrics are also introduced,and the representative COD algorithms are compared and analyzed qualitatively and quantitatively.Finally,pointed out the problems remained to be resolved and the future research directions of camouflage detection and segmentation.
作者
何淋艳
王安志
任春洪
杨元英
欧卫华
HE Lin-yan;WANG An-zhi;REN Chun-hong;YANG Yuan-ying;OU Wei-hua(School of Big Data and Computer Science,Guizhou Normal University,Guiyang 550025,China)
出处
《软件导刊》
2022年第3期237-243,共7页
Software Guide
基金
国家自然科学基金项目(61762021,61962010)
贵州省自然科学基金项目([2017]1130,[2017]5726-32)
贵州优秀青年科技人才项目([2019]-5670)
贵州师范大学2019年博士科研启动项目(GZNUD[2018]32号)
贵州省大学生创新创业训练计划项目(S202010663031)
贵州师范大学大学生科研训练计划项目(DK2019B012,DK2020B005)。
关键词
伪装检测
深度学习
注意力机制
卷积网络
camouflage detection
deep learning
attention mechanism
convolution network