A Review of YOLO Algorithm and Its Applications in Autonomous Driving Object Detection

Engineering notes

From YOLOv1 to YOLOv12, detection accuracy has improved significantly, with mAP increasing from approximately 63.4% to over 80% and inference speed exceeding 100 FPS in lightweight versions such as YOLOv8n and YOLOv10.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Object detection in autonomous driving scenarios represents a significant research direction within artificial intelligence. Real-time and accurate object detection and recognition are crucial in ensuring autonomous vehicles’ safe and stable operation. In recent years, the continuous introduction of the YOLO series of algorithms and their enhanced models has led to remarkable performance in autonomous driving object detection. From YOLOv1 to YOLOv12, detection accuracy has improved significantly, with mAP increasing from approximately 63.4% to over 80% and inference speed exceeding 100 FPS in lightweight versions such as YOLOv8n and YOLOv10. This paper reviews the YOLO algorithm and its application in object detection in autonomous driving scenarios. Firstly, the development and distinctions among the YOLO series of detection algorithms are explained, and their performance is analyzed. Secondly, the strategies for improving YOLO-based models across the input, feature extraction, and prediction stages are summarized. Thirdly, the research status and application of the YOLO algorithm in autonomous driving object detection are elaborated upon from the perspectives of traffic vehicles, pedestrians, traffic signs, traffic lights, and lane lines, with comparisons and analyses of performance metrics such as accuracy and real-time performance. Finally, considering the current challenges in autonomous driving object detection, the development trajectory and prospects of the YOLO algorithm are summarized and discussed.

Links and sources

• Official / arXiv page
• PDF from original source

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