深度学习及其在图像物体分类与检测中的应用综述

State-of-the-art on Deep Learning and its Application in Image Object Classification and Detection

传统的图像物体分类与检测算法及策略难以满足图像视频大数据在处理效率、性能和智能化等方面所提出的要求。深度学习通过模拟类似人脑的层次结构建立从低级信号到高层语义的映射,以实现数据的分级特征表达,具有强大的视觉信息处理能力,成为应对这一挑战的前沿技术和国内外研究热点。首先论述了深度学习的起源、发展历程及理论体系;然后分别围绕图像物体分类和检测,总结了近年来深度学习在视觉领域的发展;最后对深度学习及其在视觉领域目前存在的诸多问题以及后续的研究方向进行了分类探讨。

For traditional algorithms and strategies on image object classification and detection is hard to face the Challenges from efficiency,performance and intelligent of processing of image video big data Based on the simulation of a hierarchical structure existing in human brain, deep learning can establish the mapping between the low-level signals and the high-level semantics for achieving the hierarchical expression of data characteristic. Deep learning with powerful ablility for visual information processing becomes the cutting-edge technology and research hot spot in coping with the coming challenge. At first, in this paper the hasi.c theory of deep learning was discussed. Then, around image object classification and detection, we respectively summarized the development of deep learning in the visual field recentely. Finally,deep learning and its current problems in the visual field and the subsequent research direction were discussed in a well-informed level.