张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望. 自动化学报, 2017, 43(8): 1289-1305. doi: 10.16383/j.aas.2017.c160822 引用本文: 张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望. 自动化学报, 2017, 43 (8): 1289-1305. doi: 10.16383/j.aas.2017.c160822 ZHANG Hui, WANG Kun-Feng, WANG Fei-Yue. Advances and Perspectives on Applications of Deep Learning in Visual Object Detection. ACTA AUTOMATICA SINICA, 2017, 43(8): 1289-1305. doi: 10.16383/j.aas.2017.c160822 Citation: ZHANG Hui, WANG Kun-Feng, WANG Fei-Yue. Advances and Perspectives on Applications of Deep Learning in Visual Object Detection. ACTA AUTOMATICA SINICA , 2017, 43 (8): 1289-1305. doi: 10.16383/j.aas.2017.c160822 张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望. 自动化学报, 2017, 43(8): 1289-1305. doi: 10.16383/j.aas.2017.c160822 引用本文: 张慧, 王坤峰, 王飞跃. 深度学习在目标视觉检测中的应用进展与展望. 自动化学报, 2017, 43 (8): 1289-1305. doi: 10.16383/j.aas.2017.c160822 ZHANG Hui, WANG Kun-Feng, WANG Fei-Yue. Advances and Perspectives on Applications of Deep Learning in Visual Object Detection. ACTA AUTOMATICA SINICA, 2017, 43(8): 1289-1305. doi: 10.16383/j.aas.2017.c160822 Citation: ZHANG Hui, WANG Kun-Feng, WANG Fei-Yue. Advances and Perspectives on Applications of Deep Learning in Visual Object Detection. ACTA AUTOMATICA SINICA , 2017, 43 (8): 1289-1305. doi: 10.16383/j.aas.2017.c160822 作者简介:

张慧 中国科学院自动化研究所复杂系统管理与控制国家重点实验室博士研究生.主要研究方向为智能交通系统, 目标视觉检测, 深度学习.E-mail:[email protected]

王坤峰 中国科学院自动化研究所复杂系统管理与控制国家重点实验室副研究员.主要研究方向为智能交通系统, 智能视觉计算, 机器学习.E-mail:[email protected]

通讯作者: 王飞跃 中国科学院自动化研究所复杂系统管理与控制国家重点实验室研究员.国防科学技术大学军事计算实验与平行系统技术研究中心主任.主要研究方向为智能系统和复杂系统的建模、分析与控制.本文通信作者.E-mail: [email protected] 目标视觉检测 /  深度学习 /  计算机视觉 / Abstract: Visual object detection is an important topic in computer vision, and has great theoretical and practical merits in applications such as visual surveillance, autonomous driving, and human-machine interaction. In recent years, significant breakthroughs of deep learning methods in image recognition research have arisen much attention of researchers and accordingly led to the rapid development of visual object detection. In this paper, we review the current advances and perspectives on the applications of deep learning in visual object detection. Firstly, we present the basic procedure for visual object detection and introduce some newly emerging and commonly used data sets. Then we detail the applications of deep learning techniques in visual object detection. Finally, we make in-depth discussions about the difficulties and challenges brought by deep learning as applied to visual object detection, and propose some perspectives on future trends. Key words: Visual object detection /  deep learning /  computer vision /  parallel vision  黄凯奇, 任伟强, 谭铁牛.图像物体分类与检测算法综述.计算机学报, 2014, 37(6):1225-1240 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJX201406001.htm

Huang Kai-Qi, Ren Wei-Qiang, Tan Tie-Niu. A review on image object classification and detection. Chinese Journal of Computers, 2014, 37(6):1225-1240 http://www.cnki.com.cn/Article/CJFDTOTAL-JSJX201406001.htm Ojala T, Pietikäinen M, Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In:Proceedings of the 12th IAPR International Conference on Pattern Recognition, Conference A:Computer Vision and Image Processing. Jerusalem, Israel, Palestine:IEEE, 1994, 1:582-585 Hinton G, Deng L, Yu D, Dahl G E, Mohamed A R, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath T N, Kingsbury B. Deep neural networks for acoustic modeling in speech recognition:the shared views of four research groups. IEEE Signal Processing Magazine, 2012, 29(6):82-97 doi: 10.1109/MSP.2012.2205597 王坤峰, 苟超, 王飞跃.平行视觉:基于ACP的智能视觉计算方法.自动化学报, 2016, 42(10):1490-1500 http://www.aas.net.cn/CN/abstract/abstract18936.shtml

Wang Kun-Feng, Gou Chao, Wang Fei-Yue. Parallel vision:an ACP-based approach to intelligent vision computing. Acta Automatica Sinica, 2016, 42(10):1490-1500 http://www.aas.net.cn/CN/abstract/abstract18936.shtml 王飞跃.平行系统方法与复杂系统的管理和控制.控制与决策, 2004, 19(5):485-489, 514 http://www.cnki.com.cn/Article/CJFDTOTAL-KZYC200405001.htm

Wang Fei-Yue. Parallel system methods for management and control of complex systems. Control and Decision, 2004, 19(5):485-489, 514 http://www.cnki.com.cn/Article/CJFDTOTAL-KZYC200405001.htm

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