Scholarlink

<p>Damaged footpaths, including cracks and surface irregularities, pose significant risks to pedestrians in urban environments. To facilitate research in automated infrastructure monitoring and pedestrian fall-risk mitigation, we introduce the Indian Footpath Damage Segmentation Dataset, a high-resolution image collection captured using a professional SLR camera under diverse lighting and weather conditions in Pune, India. Each image has been manually annotated with pixel-level segmentation masks using the LabelMe tool, targeting a single class: footpath damage. Annotations include severity levels (low, medium, high) based on crack width, defined in consultation with civil engineering experts. To demonstrate the dataset’s utility, we trained a U-Net model with an EfficientNet-B3 encoder and attention-based decoder, and further experimented with ResNeXt50_32x4d encoders and ensemble methods incorporating a convolutional fusion head. Our best-performing ensemble model achieved a Dice score of 0.6899, Intersection over Union (IoU) of 0.6741, accuracy of 0.9317, precision of 0.7154, recall of 0.6932, and F1-score of 0.6899. The dataset is openly accessible for academic and research use on Zenodo.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Context-Aware Deep Learning based Indian Footpath Damage Segmentation Dataset for Risk Assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

查看全文
  • Priti Chakurkar,
  • Deepali Vora

摘要

Damaged footpaths, including cracks and surface irregularities, pose significant risks to pedestrians in urban environments. To facilitate research in automated infrastructure monitoring and pedestrian fall-risk mitigation, we introduce the Indian Footpath Damage Segmentation Dataset, a high-resolution image collection captured using a professional SLR camera under diverse lighting and weather conditions in Pune, India. Each image has been manually annotated with pixel-level segmentation masks using the LabelMe tool, targeting a single class: footpath damage. Annotations include severity levels (low, medium, high) based on crack width, defined in consultation with civil engineering experts. To demonstrate the dataset’s utility, we trained a U-Net model with an EfficientNet-B3 encoder and attention-based decoder, and further experimented with ResNeXt50_32x4d encoders and ensemble methods incorporating a convolutional fusion head. Our best-performing ensemble model achieved a Dice score of 0.6899, Intersection over Union (IoU) of 0.6741, accuracy of 0.9317, precision of 0.7154, recall of 0.6932, and F1-score of 0.6899. The dataset is openly accessible for academic and research use on Zenodo.