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Instance Segmentation Advances Pavement Crack Assessment

By PulseAugur Editorial · [4 sources] ·

A new paper details a vision-based system for pavement distress assessment that utilizes Mask R-CNN instance segmentation for precise crack localization. This approach significantly outperforms traditional object detection methods, achieving high precision and recall on a custom dataset. The research highlights instance segmentation as a practical method for analyzing field pavement imagery and estimating crack areas, while also identifying areas for future improvement such as annotation consistency and class imbalance. AI

IMPACT This research demonstrates the effectiveness of instance segmentation for detailed pavement analysis, potentially improving infrastructure maintenance.

RANK_REASON The cluster contains a research paper detailing a new methodology for pavement distress assessment using AI.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Instance Segmentation Advances Pavement Crack Assessment

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The cluster contains a research paper detailing a new methodology for pavement distress assessment using AI.
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COVERAGE [4]

  1. Hugging Face Daily Papers TIER_1 Français(FR) ·

    Pixel-Level Pavement Distress Assessment Using Instance Segmentation

    Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localization of thin, branching, and irregular cracks to achieve the geometric precision necessary for maintenance-relevant quantification. Thi…

  2. Hugging Face Daily Papers TIER_1 Français(FR) ·

    Pixel-Level Pavement Distress Assessment Using Instance Segmentation

    A vision-based pavement distress analysis system using Mask R-CNN instance segmentation demonstrates superior performance for crack detection and quantification compared to object detection approaches, achieving high precision and recall metrics on a custom field-collected datase…

  3. arXiv cs.CV TIER_1 Français(FR) · Logan Dewick (University of Wisconsin - Green Bay), Bibesh Pyakurel (University of Wisconsin - Green Bay), Kong Pheng Yang (University of Wisconsin - Green Bay), Nazim Choudhury (University of Wisconsin - Green Bay), M. G. Sarwar Murshed (University of W… ·

    Pixel-Level Pavement Distress Assessment Using Instance Segmentation

    arXiv:2605.26095v1 Announce Type: new Abstract: Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localization of thin, branching, and irregular cracks to achieve the geometric precision necess…

  4. arXiv cs.CV TIER_1 Français(FR) · M. G. Sarwar Murshed ·

    Pixel-Level Pavement Distress Assessment Using Instance Segmentation

    Automated pavement distress assessment requires more than image-level classification or coarse bounding box detection, demanding precise localization of thin, branching, and irregular cracks to achieve the geometric precision necessary for maintenance-relevant quantification. Thi…

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