电子测量与仪器学报

卢笑,曹意宏,周炫余,王耀南.基于深度强化学习的两阶段显著性目标检测[J].电子测量与仪器学报,2021,35(6):34-42

基于深度强化学习的两阶段显著性目标检测

Two-stage salient object detection with deep reinforcement learning

DOI：

英文关键词:salient object detection deep reinforcement learning(DRL) Markov decision process convolutional neural network

基金项目:国家自然科学基金（62007007，61703155）、湖南省自然科学基金(2018JJ3350，2018JJ3352)项目资助

作者	单位
卢笑	1. 湖南师范大学工程与设计学院
曹意宏	1. 湖南师范大学工程与设计学院
周炫余	2. 湖南师范大学基础教育大数据研究与应用重点实验室
王耀南	3. 湖南大学机器人视觉感知与控制技术国家工程实验室

Author	Institution
Lu Xiao	1. College of Engineering and Design, Hunan Normal University
Cao Yihong	1. College of Engineering and Design, Hunan Normal University
Zhou Xuanyu	2. Key Laboratory of Big Data Research and Application for Basic Education, Hunan Normal University
Wang Yaonan	3. National Engineering Laboratory for Robot Visual Perception and Control Technology, Hunan University

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中文摘要:

为提高复杂场景下的显著性目标检测速度和精度,提出了一种基于深度强化学习的两阶段显著性目标检测方法。该算法由显著性区域定位网络(salient region localization network, SRLN)和显著性目标分割网络( salient object segmentation network, SOSN)组成,分别对应显著性区域定位阶段和显著性目标分割阶段。在显著性区域定位阶段,首次提出采用深度强化学习训练智能体通过执行序列动作逐步定位显著性区域。再将其交由分割网络进行第二阶段的精细目标分割。网络结构上,SRLN 和 SOSN 采用共享特征提取网络的方式简化模型和减少参数量,同时针对该两阶段检测框架提出了一种分治的训练策略。在公开的显著性目标检测数据集上的实验结果表明,无论是简单或复杂场景的图像,该算法能够快速有效的剔除干扰信息,获得准确的显著性目标检测结果,并且检测速度达到了实时性能。在行人检测数据集上的检测结果表明本算法在其他实际应用问题上也具有较强的泛化能力。

英文摘要:

To improve the speed and accuracy of salient object detection in complex scenes, a two-stage salient object detection method based on deep reinforcement learning is proposed. It is composed of the salient region location network (SRLN) and the salient object segmentation network (SOSN), corresponding to the salient region location stage and the salient object segmentation stage respectively. In the salient region location stage, deep reinforcement learning (DRL) is used to train an agent to locate to the salient region gradually through sequence actions. To the best of our knowledge, it is the first to use DRL-based method for salient object detection. Then the fine salient object segmentation result can be obtained via a simple segmentation network. To simplify the network structure and reduce the number of parameters, SRLN and SOSN share feature extraction network. At the same time, a divide-and-conquer training strategy is proposed for the two-stage salient object detection framework. Experimental results on public datasets show that no matter for simple or complex scene images, the proposed algorithm can eliminate the disturbing information effectively and rapidly, achieving accurate and real-time performance. The extended experiments on pedestrian detection dataset show the generalization ability of our method in other application problems.

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