用多边形标注提升道路损毁与井盖检测精度，适合智慧城市维护。
Road Damage and Manhole Detection using Deep Learning for Smart Cities: A Polygonal Annotation Approach
- 采用多边形标注替代框选，定位更精准。
- 整体图像准确率达78.1%，破損类F1达86.7%。
- 专为发展中国家城市维护设计，可扩展性强。
城市安全与基础设施维护是智慧城市建设的关键。人工监测道路损毁耗时长、成本高且易出错。本文提出基于YOLOv9的深度学习方法，使用多边形标注实现道路缺陷与井盖的自动检测。不同于传统边界框标注，多边形标注能更精确地定位缺陷。我们构建了一个包含一千多张图像的新数据集，主要采集于孟加拉国达卡市。该数据集用于训练一个三分类模型（破碎、未破碎、井盖）。整体图像级准确率达到78.1%。YOLOv9在破碎类（F1-score 86.7%）和未破碎类（F1-score 89.2%）表现良好，但井盖检测受限于类别不平衡，仅达18.2%的F1-score。该方法为发展中国家城市基础设施监控提供了高效、可扩展的解决方案。
原文摘要 · Abstract (English)
Urban safety and infrastructure maintenance are critical components of smart city development. Manual monitoring of road damages is time-consuming, highly costly, and error-prone. This paper presents a deep learning approach for automated road damage and manhole detection using the YOLOv9 algorithm with polygonal annotations. Unlike traditional bounding box annotation, we employ polygonal annotations for more precise localization of road defects. We develop a novel dataset comprising more than one thousand images which are mostly collected from Dhaka, Bangladesh. This dataset is used to train a YOLO-based model for three classes, namely Broken, Not Broken, and Manhole. We achieve 78.1% overall image-level accuracy. The YOLOv9 model demonstrates strong performance for Broken (86.7% F1-score) and Not Broken (89.2% F1-score) classes, with challenges in Manhole detection (18.2% F1-score) due to class imbalance. Our approach offers an efficient and scalable solution for monitoring urban infrastructure in developing countries.
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