Electric scooters (e-scooters) have rapidly emerged as a popular mode of transportation in urban areas, yet they pose significant safety challenges. In the United States, the rise of e-scooters has been marked by a concerning increase in related injuries and fatalities. Recently, while deep-learning object detection holds paramount significance in autonomous vehicles to avoid potential collisions, its application in the context of e-scooters remains relatively unexplored. This paper addresses this gap by assessing the effectiveness and efficiency of cutting-edge object detectors designed for e-scooters. To achieve this, the first comprehensive benchmark involving 22 state-of-the-art YOLO object detectors, including five versions (YOLOv3, YOLOv5, YOLOv6, YOLOv7, and YOLOv8), has been established for real-time traffic object detection using a self-collected dataset featuring e-scooters. The detection accuracy, measured in terms of mAP@0.5, ranges from 27.4% (YOLOv7-E6E) to 86.8% (YOLOv5s). All YOLO models, particularly YOLOv3-tiny, have displayed promising potential for real-time object detection in the context of e-scooters. Both the traffic scene dataset (https://zenodo.org/records/10578641) and software program codes (https://github.com/DongChen06/ScooterDet) for model benchmarking in this study are publicly available, which will not only improve e-scooter safety with advanced object detection but also lay the groundwork for tailored solutions, promising a safer and more sustainable urban micromobility landscape.

本研究通过评估适用于电动滑板车的前沿目标检测器的效果和效率，建立了首个全面的基准，其中包括22种最新的YOLO目标检测器，包括YOLOv3、YOLOv5、YOLOv6、YOLOv7和YOLOv8等五个版本，使用自采集的电动滑板车数据集进行实时交通目标检测。检测的准确性以mAP@0.5的方式进行衡量，范围从27.4%（YOLOv7-E6E）到86.8%（YOLOv5s）。所有的YOLO模型，尤其是YOLOv3-tiny，在电动滑板车的实时目标检测方面显示出了具有潜力的前景。本研究的交通场景数据集和软件程序代码都是公开可用的，这将不仅提高电动滑板车的安全性，还为定制解决方案奠定基础，为更安全和可持续的城市微移动交通方式打下坚实的基础。

电动滑板车实时目标检测性能评估