YOLO算法及其在自动驾驶场景中目标检测综述

Review of YOLO algorithm and its applications to object detection in autonomous driving scenes

自动驾驶场景下的目标检测是计算机视觉中重要研究方向之一,确保自动驾驶汽车对物体进行实时准确的目标检测是研究重点。近年来,深度学习技术迅速发展并被广泛应用于自动驾驶领域中,极大促进了自动驾驶领域的进步。为此,针对YOLO(You Only Look Once)算法在自动驾驶领域中的目标检测研究现状,从以下4个方面分析。首先,总结单阶段YOLO系列检测算法思想及其改进方法,分析YOLO系列算法的优缺点;其次,论述YOLO算法在自动驾驶场景下目标检测中的应用,从交通车辆、行人和交通信号识别这3个方面分别阐述和总结研究现状及应用情况;此外,总结目标检测中常用的评价指标、目标检测数据集和自动驾驶场景数据集;最后,展望目标检测存在的问题和未来发展方向。

Object detection in autonomous driving scenes is one of the important research directions in computer vision.The researches focus on ensuring real-time and accurate object detection of objects by autonomous vehicles.Recently,a rapid development in deep learning technology had been witnessed,and its wide application in the field of autonomous driving had prompted substantial progress in this field.An analysis was conducted on the research status of object detection by YOLO(You Only Look Once)algorithms in the field of autonomous driving from the following four aspects.Firstly,the ideas and improvement methods of the single-stage YOLO series of detection algorithms were summarized,and the advantages and disadvantages of the YOLO series of algorithms were analyzed.Secondly,the YOLO algorithm-based object detection applications in autonomous driving scenes were introduced,the research status and applications for the detection and recognition of traffic vehicles,pedestrians,and traffic signals were expounded and summarized respectively.Additionally,the commonly used evaluation indicators in object detection,as well as the object detection datasets and automatic driving scene datasets,were summarized.Lastly,the problems and future development directions of object detection were discussed.