使用深度学习和多传感器融合的电动滑板车实时道路障碍物检测
Real-Time Roadway Obstacle Detection for Electric Scooters Using Deep Learning and Multi-Sensor Fusion
AI总结：
本研究提出一种基于深度学习和多传感器融合的电动滑板车地面障碍物检测系统，利用YOLO模型和Intel RealSense D435i摄像头，在7小时自然骑行数据集上实现0.827的mAP，有效提升了实时道路危险检测能力。
摘要：
电动滑板车在城市地区的日益普及与交通事故和伤害的增加相吻合，这主要是由于其车轮小、缺乏悬挂系统以及对不平整表面的敏感性。虽然基于深度学习的物体检测已广泛用于提高汽车安全性，但其在电动滑板车障碍物检测中的应用仍未被探索。本研究引入了一种用于电动滑板车的新型地面障碍物检测系统，集成了RGB摄像头和深度摄像头，以增强实时道路危险检测。此外，惯性测量单元（IMU）测量线性垂直加速度以识别表面振动，指导了六个障碍物类别的选择：树枝、井盖、坑洼、松果、非定向裂缝和截顶圆顶。包括RGB摄像头、深度摄像头和IMU在内的所有传感器都集成在Intel RealSense Camera D435i内。由YOLO驱动的深度学习模型检测道路危险，并利用深度数据估计障碍物的接近程度。在7小时的自然骑行数据集上进行评估，该系统实现了0.827的高平均精度均值（mAP），并展现出优异的实时性能。该方法通过先进的计算机视觉和数据融合，为增强电动滑板车安全性提供了有效解决方案。数据集可在https://zenodo.org/records/14583718获取，项目代码托管在https://github.com/Zeyang-Zheng/Real-Time-Roadway-Obstacle-Detection-for-Electric-Scooters。
英文摘要：
The increasing adoption of electric scooters (e-scooters) in urban areas has coincided with a rise in traffic accidents and injuries, largely due to their small wheels, lack of suspension, and sensitivity to uneven surfaces. While deep learning-based object detection has been widely used to improve automobile safety, its application for e-scooter obstacle detection remains unexplored. This study introduces a novel ground obstacle detection system for e-scooters, integrating an RGB camera, and a depth camera to enhance real-time road hazard detection. Additionally, the Inertial Measurement Unit (IMU) measures linear vertical acceleration to identify surface vibrations, guiding the selection of six obstacle categories: tree branches, manhole covers, potholes, pine cones, non-directional cracks, and truncated domes. All sensors, including the RGB camera, depth camera, and IMU, are integrated within the Intel RealSense Camera D435i. A deep learning model powered by YOLO detects road hazards and utilizes depth data to estimate obstacle proximity. Evaluated on the seven hours of naturalistic riding dataset, the system achieves a high mean average precision (mAP) of 0.827 and demonstrates excellent real-time performance. This approach provides an effective solution to enhance e-scooter safety through advanced computer vision and data fusion. The dataset is accessible at https://zenodo.org/records/14583718, and the project code is hosted on https://github.com/Zeyang-Zheng/Real-Time-Roadway-Obstacle-Detection-for-Electric-Scooters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
