2024/10/30 by Mujadded Al Rabbani Alif, Alif, Mujadded Al Rabbani · 1 citation
Computer Science · Engineering · #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection
paper · pdf · doi:10.48550/arxiv.2410.22898
openalex publication_date 2024/10/30 · openalex created_date 2024/11/14 · openalex updated_date 2026/07/28
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.