Review of YOLO Based Target Detection for Autonomous Driving

doi:10.3778/j.issn.1002-8331.2203-0310

Abstract

Abstract: Autonomous driving is an important direction of artificial intelligence and has good prospects. Target detection with real-time and accurate is essential to ensure the safe and stable of autonomous driving. This paper reviews the development history of autonomous driving and target detection technologies, then discusses the applications of target detection based on YOLO algorithm, including vehicles, pedestrians, lane lines, traffic signs and lights. Meanwhile, the accurate and real-time performances are compared and analyzed. This paper sheds light on the challenges, possible solutions and potential directions for future research.

Key words: autonomous driving, target detection, YOLO algorithm

摘要： 自动驾驶是人工智能发展领域的一个重要方向，拥有良好的发展前景，而实时准确的目标检测与识别是保证自动驾驶汽车安全稳定运行的基础与关键。回顾自动驾驶和目标检测技术的发展历程，综述了YOLO算法在车辆、行人、交通标志、灯光、车道线等目标检测上的应用，同时对比分析了精确性与实时性等性能，阐述了自动驾驶目标检测研究领域将要面临的挑战、可能的解决方案和潜在的发展方向。

关键词: 自动驾驶, 目标检测, YOLO算法

MAO Zhihui, ZHU Jiali, WU Xin, LI Jun. Review of YOLO Based Target Detection for Autonomous Driving[J]. Computer Engineering and Applications, 2022, 58(15): 68-77.

茅智慧, 朱佳利, 吴鑫, 李君. 基于YOLO的自动驾驶目标检测研究综述[J]. 计算机工程与应用, 2022, 58(15): 68-77.

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