论文标题
实时道路基础设施管理的计算机视觉辅助方法
A Computer Vision-assisted Approach to Automated Real-Time Road Infrastructure Management
论文作者
论文摘要
准确的自动检测道路路面障碍对于及时识别和修复可能引起事故的道路危害（例如坑洼和其他表面水平沥青裂纹）至关重要。在低资源环境中，部署这种系统将是进一步的优势，因为缺乏政府用于基础设施维护的资金通常会导致由于不足和不经常对道路危害的道路系统进行手动检查，可能会增加潜在致命的车辆道路事故的风险。为了解决这个问题，电气和电子工程师研究所（“ IEEE”）最新的一项研究计划是2020年5月出版的2020年全球道路损害检测（“ GRDC”）挑战的一部分，该挑战于2020年5月发表，这是一本小说的21,041个注释的注释图像数据集，该数据集的各种道路遇险的各种道路遇险呼吁对这些知识和其他研究人员提出创新的基于知识的道路解决方案，以提交这些问题，以提交这些问题，以提交这些问题。利用该数据集，我们提出了一种监督的对象检测方法，仅利用您一次（“ Yolo”）和更快的R-CNN框架来通过车辆仪表板安装的智能手机相机实时检测和分类道路遇险，并在121个团队中产生0.68 F1量级实验结果在121个团队中排名为121个挑战，这一挑战是1221年的挑战。
Accurate automated detection of road pavement distresses is critical for the timely identification and repair of potentially accident-inducing road hazards such as potholes and other surface-level asphalt cracks. Deployment of such a system would be further advantageous in low-resource environments where lack of government funding for infrastructure maintenance typically entails heightened risks of potentially fatal vehicular road accidents as a result of inadequate and infrequent manual inspection of road systems for road hazards. To remedy this, a recent research initiative organized by the Institute of Electrical and Electronics Engineers ("IEEE") as part of their 2020 Global Road Damage Detection ("GRDC") Challenge published in May 2020 a novel 21,041 annotated image dataset of various road distresses calling upon academic and other researchers to submit innovative deep learning-based solutions to these road hazard detection problems. Making use of this dataset, we propose a supervised object detection approach leveraging You Only Look Once ("YOLO") and the Faster R-CNN frameworks to detect and classify road distresses in real-time via a vehicle dashboard-mounted smartphone camera, producing 0.68 F1-score experimental results ranking in the top 5 of 121 teams that entered this challenge as of December 2021.
