摘要
全卷积神经网络(FCN)高效的特征提取能力极大地提升了显著性目标检测算法的性能。然而现有算法依靠简单的特征拼接或相加等融合策略无法有效地增强特征,导致算法在复杂场景中的目标误检和漏检问题依然突出。文中提出基于场景的针对性特征增强方法来提高显著性目标检测算法的性能。首先,目标误检多发生于背景复杂且目标和背景元素交织的场景,文中分别从特征全局性增强和特征结构化增强角度解决目标误检问题;其次,针对目标漏检一般发生在目标的内部和边缘,基于残差学习从背景中学习丢失目标的信息,修复丢失的目标内部和边缘区域;最后,在5个基准数据集上与其他13种先进的方法进行实验对比,结果表明文中所提模型的各性能评价指标均优于其他13种方法,显著地解决了复杂场景中的目标误检和漏检问题。
The performance of salient object detection is greatly improved by the superior feature extraction ability of Fully Convolutional Neural Networks(FCN).However,the simple fusion strategies(feature addition or concatenation)cannot effectively enhance features,resulting in algorithm's object misdetection and missed detection in complex scenes.The paper proposed a specifically feature enhancement method to improve the performance of salient object detection.Firstly,object misdetection mostly occurs in a scene where the background is cluttered or the object and the background are intertwined,so it greatly alleviate the object misdetection problem from the perspective of global enhancement and structural enhancement,respectively.Secondly,the missed detection of the object generally occurs in the interior and edge of the object,so the study introduce residual learning to learn the information of the missed region and refine the loss of the object interior and edge.Finally,comparison results between the proposed method with other 13 kinds of advanced methods over 5 benchmark datasets indicate that the proposed model is superior to other 13 methods,and the problems of object misdetection and missed detection in complex scenes were successfully solved.
作者
李波
饶浩波
LI Bo;RAO Haobo(School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2021年第11期135-144,共10页
Journal of South China University of Technology(Natural Science Edition)
基金
国家重点研发计划项目(2017YFC0806000)
国家自然科学基金资助项目(11627802,51678249)
教育部留学基金资助项目(201806155022)。
关键词
全卷积神经网络
显著性目标检测
特征增强
目标误检
目标漏检
fully convolutional neural networks
salient object detection
feature enhancement
object misdetection
missed detection
