伪装目标检测与分割研究进展

Research Advances in Camouflage Object Detection and Segmentation

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.