用行车记录仪自动检测道路坑洼并实时地图标注，助力智慧城市建设
iWatchRoad: Scalable Detection and Geospatial Visualization of Potholes for Smart Cities
- 基于7000+帧自标注数据训练YOLO模型，实现复杂环境下实时坑洼检测
- 通过视频时间戳与GPS同步，精准定位每处坑洼的地理坐标
- 低成本可扩展，适合城乡道路管理，政府可用数据支持维护决策
道路坑洼是严重的安全隐患和养护负担，尤其在印度多样且维护不足的道路环境中更为突出。本文提出端到端系统iWatchRoad，实现自动化坑洼检测、全球定位系统（GPS）标记及基于开放街图（OSM）的实时地图可视化。我们采集了超过7000帧覆盖多种道路类型、光照与天气条件的行车记录视频，构建了独特的自标注数据集。该数据用于微调Ultralytics YOLO模型以实现实时坑洼识别，同时采用定制光学字符识别（OCR）模块从视频帧中提取时间戳，并与GPS日志同步，实现坑洼精准地理标记。处理后的数据连同图像元信息存储于数据库，通过友好的网页界面在OSM上可视化呈现。iWatchRoad不仅在复杂条件下提升检测准确率，还提供政府可用的结构化输出，支持道路评估与养护规划。系统成本低、硬件高效、可扩展，为发展中国家城乡道路管理提供实用自动化工具。
原文摘要 · Abstract (English)
Potholes on the roads are a serious hazard and maintenance burden. This poses a significant threat to road safety and vehicle longevity, especially on the diverse and under-maintained roads of India. In this paper, we present a complete end-to-end system called iWatchRoad for automated pothole detection, Global Positioning System (GPS) tagging, and real time mapping using OpenStreetMap (OSM). We curated a large, self-annotated dataset of over 7,000 frames captured across various road types, lighting conditions, and weather scenarios unique to Indian environments, leveraging dashcam footage. This dataset is used to fine-tune, Ultralytics You Only Look Once (YOLO) model to perform real time pothole detection, while a custom Optical Character Recognition (OCR) module was employed to extract timestamps directly from video frames. The timestamps are synchronized with GPS logs to geotag each detected potholes accurately. The processed data includes the potholes' details and frames as metadata is stored in a database and visualized via a user friendly web interface using OSM. iWatchRoad not only improves detection accuracy under challenging conditions but also provides government compatible outputs for road assessment and maintenance planning through the metadata visible on the website. Our solution is cost effective, hardware efficient, and scalable, offering a practical tool for urban and rural road management in developing regions, making the system automated. iWatchRoad is available at https://smlab.niser.ac.in/project/iwatchroad
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