关键词: 关键词: 目标检测, 自动驾驶, 实时检测, YOLO算法, 交通场景

Abstract: Object detection in autonomous driving scenarios was acknowledged as an important research direction in computer vision. The research focus was positioned on ensuring the 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 ed substantial progress in this domain. In order to analyze this, an analysis was conducted on the research status of the You Only Look Once (YOLO) algorithms object detection in the field of autonomous driving from the following four aspects. Firstly, a summary was provided for the ideas and improvement methods of the single-stage YOLO series of detection algorithms, and an analysis was carried out on the advantages and disadvantages of the YOLO series of algorithms. Secondly, a discussion was initiated on the YOLO algorithm-based object detection application in automatic driving scenarios, focusing on traffic vehicles, pedestrian recognition, and traffic signal analysis. Expositions and summaries were provided respectively for the research statuses and application situations of each aspect. Additionally, the commonly used evaluation indicators in object detection, as well as the object detection datasets and automatic driving scene datasets, were consolidated. Lastly, a projection on the future direction and the identified challenges in object detection was provided.

Key words: Keywords: object detection, autonomous driving, real-time detection, YOLO (You Only Look Once) algorithm, traffic scene