目标检测是计算机视觉领域中的研究热点. 近年来, 目标检测的深度学习算法有突飞猛进的发展. 基于深度学习的目标检测算法大致可分为基于候选区域和基于回归两大类. 基于候选区域的目标检测算法精度高, 但是结构复杂, 检测速度较慢. 而基于回归的目标检测算法结构简单、检测速度快, 在实时目标检测领域有较高的应用价值, 然而检测精度相对略低. 本文总结了基于深度学习的目标检测主流算法, 并分析了相关算法的优缺点和应用场景. 最后根据深度学习的目标检测算法中存在的困难和挑战, 对未来的发展趋势做了思考和展望.
Object detection is a research hotspot in the field of computer vision. In recent years, the deep learning algorithms contributing to object detection has developed by leaps and bounds. Objection detection algorithms based on deep learning can be roughly divided into two categories depending on candidate regions and regression, respectively. The object detection algorithms based on candidate regions have high accuracy, but complex structure and low speed of detection. The object detection algorithms based on regression, contrarily, have simple structure, high speed of detection, and thus more applications in the field of real-time object detection, but its detection is with low accuracy. This paper summarizes the mainstream algorithms of object detection based on deep learning and analyzes the advantages and disadvantages of different algorithms and their applications. Finally, this paper predicts the prospects of deep learning-based object detection algorithms according to the existing challenges.