自动化学报2017，Vol.43Issue(8)：1289-1305,17.DOI:10.16383/j.aas.2017.c160822
深度学习在目标视觉检测中的应用进展与展望
Advances and Perspectives on Applications of Deep Learning in Visual Object Detection
摘要
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.关键词
目标视觉检测/深度学习/计算机视觉/平行视觉Key words
Visual object detection/deep learning/computer vision/parallel vision引用本文复制引用
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[J].自动化学报,2017,43(8):1289-1305,17.基金项目
国家自然科学基金(61533019, 61304200), 国家留学基金(201504910397) 资助Supported by National Natural Science Foundation of China (61533019, 61304200) and China Scholarship Council (201504910397) （61533019, 61304200）
