Pixel-Level Pavement Distress Assessment Using Instance Segmentation

Fuente: arXiv

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Bibliographic Details
Main Authors:	Dewick, Logan, Pyakurel, Bibesh, Yang, Kong Pheng, Choudhury, Nazim, Murshed, M. G. Sarwar
Format:	Preprint
Published:	2026
Subjects:	Computer Vision and Pattern Recognition I.4.8; I.2.10
Online Access:	Acceder al recurso
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author	Dewick, Logan Pyakurel, Bibesh Yang, Kong Pheng Choudhury, Nazim Murshed, M. G. Sarwar
author_facet	Dewick, Logan Pyakurel, Bibesh Yang, Kong Pheng Choudhury, Nazim Murshed, M. G. Sarwar
contents	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. This paper presents a vision-based pavement distress analysis system based on Mask R-CNN instance segmentation and evaluates it on UWGB-StreetCrack, a custom field-collected roadway image dataset acquired with a vehicle-mounted smartphone and manually annotated with polygon labels for longitudinal cracks, transverse cracks, alligator cracks, and potholes. Five Detectron2-based Mask R-CNN backbone variants were considered under a consistent fine-tuning protocol. The best-performing model, Mask R-CNN with a ResNet-101 FPN backbone, achieved 84.23% precision, 90.04% recall, and an F1 score of 87.04% under the project-specific bounding-box matching protocol. The same model produced an aggregate predicted crack-area fraction of 2.164%, closely matching the 2.170% ground-truth crack-area fraction. To contextualize the segmentation system against a detector-oriented alternative, a CSPDarknet53-based YOLO detector was also adapted and retrained on the dataset, reaching 27.5% precision and 20.7% recall on the validation protocol. The results show that instance segmentation is a practical direction for field pavement imagery and aggregate crack-area estimation, while also exposing open challenges in annotation consistency, class imbalance, confounder rejection, and mask-level benchmarking.
format	Preprint
id	arxiv_https___arxiv_org_abs_2605_26095
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Pixel-Level Pavement Distress Assessment Using Instance Segmentation Dewick, Logan Pyakurel, Bibesh Yang, Kong Pheng Choudhury, Nazim Murshed, M. G. Sarwar Computer Vision and Pattern Recognition I.4.8; I.2.10 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. This paper presents a vision-based pavement distress analysis system based on Mask R-CNN instance segmentation and evaluates it on UWGB-StreetCrack, a custom field-collected roadway image dataset acquired with a vehicle-mounted smartphone and manually annotated with polygon labels for longitudinal cracks, transverse cracks, alligator cracks, and potholes. Five Detectron2-based Mask R-CNN backbone variants were considered under a consistent fine-tuning protocol. The best-performing model, Mask R-CNN with a ResNet-101 FPN backbone, achieved 84.23% precision, 90.04% recall, and an F1 score of 87.04% under the project-specific bounding-box matching protocol. The same model produced an aggregate predicted crack-area fraction of 2.164%, closely matching the 2.170% ground-truth crack-area fraction. To contextualize the segmentation system against a detector-oriented alternative, a CSPDarknet53-based YOLO detector was also adapted and retrained on the dataset, reaching 27.5% precision and 20.7% recall on the validation protocol. The results show that instance segmentation is a practical direction for field pavement imagery and aggregate crack-area estimation, while also exposing open challenges in annotation consistency, class imbalance, confounder rejection, and mask-level benchmarking.
title	Pixel-Level Pavement Distress Assessment Using Instance Segmentation
topic	Computer Vision and Pattern Recognition I.4.8; I.2.10
url	https://arxiv.org/abs/2605.26095