Accurate vehicle detection is essential for the development of intelligent transportation systems, autonomous driving, and traffic monitoring. This paper presents a detailed analysis of YOLO11, the latest advancement in the YOLO series of deep learning models, focusing exclusively on vehicle detection tasks. Building upon the success of its predecessors, YOLO11 introduces architectural improvements designed to enhance detection speed, accuracy, and robustness in complex environments. Using a comprehensive dataset comprising multiple vehicle types-cars, trucks, buses, motorcycles, and bicycles we evaluate YOLO11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLO11 surpasses previous versions (YOLOv8 and YOLOv10) in detecting smaller and more occluded vehicles while maintaining a competitive inference time, making it well-suited for real-time applications. Comparative analysis shows significant improvements in the detection of complex vehicle geometries, further contributing to the development of efficient and scalable vehicle detection systems. This research highlights YOLO11's potential to enhance autonomous vehicle performance and traffic monitoring systems, offering insights for future developments in the field.

该研究解决了智能交通系统、自动驾驶和交通监测中车辆检测准确性不足的问题。YOLO11作为YOLO系列最新的深度学习模型，通过架构改进，提高了在复杂环境下的检测速度和准确性。研究结果表明，YOLO11在检测较小和被遮挡车辆方面超过了之前的版本，为自动驾驶和交通监测系统的效率提升提供了新的视角。

YOLOv11在车辆检测中的应用：进展、性能与智能交通系统的应用