YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systems

TL;DR

This paper introduces YOLOv11, an improved vehicle detection model that outperforms previous versions in accuracy and robustness, especially for small and occluded vehicles, suitable for real-time traffic and autonomous systems.

Contribution

YOLOv11 presents architectural enhancements that significantly improve detection accuracy and robustness for complex vehicle scenarios compared to prior YOLO models.

Findings

YOLOv11 surpasses YOLOv8 and YOLOv10 in detection accuracy.

YOLOv11 maintains real-time inference speed.

Enhanced detection of small and occluded vehicles.

Abstract

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.