Deep Learning Image Analysis for Pedestrian and Animal Safety in Traffic intersection using YOLOv10 and YOLOv11
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Keywords:
Deep Learning, Object Detection, Pedestrian and Animal Detection, Traffic Safety, YOLOv10, YOLOv11Abstract
Ensuring the safety of pedestrians and animals at traffic intersections remains a critical
challenge for intelligent transportation systems, particularly in complex urban environments. Traditional
object detection methods often fail to deliver reliable real-time performance. However, recent
advancements in deep learning, particularly within the YOLO (You Only Look Once) family, have
significantly improved detection speed and accuracy. While earlier versions, such as YOLOv5 to
YOLOv9, have been extensively studied, the potential of the more recent YOLOv10 and YOLOv11
architectures remains largely unexplored. This study provides a comparative analysis of these models in
the context of detecting pedestrians and animals at intersections. The results show that YOLOv11
achieves an impressive mean Average Precision (mAP) of 89%, outperforming previous YOLO versions
and current state-of-the-art methods. This positions YOLOv11 as a strong candidate for real-time traffic
safety applications. The findings underscore the potential of cutting-edge deep learning models in
advancing traffic safety and contribute to the development of smarter, more responsive transport
infrastructure.
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