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<p>Pavement condition monitoring is an important aspect of an efficient Pavement Management System (PMS). However, the monitoring of road sections is a tedious work. Therefore, research communities have come up with deep learning based automatic methods for detecting and classifying road damages. But for a highway authority overlooking a large network of roads, an aggregate measure of road condition is more useful. Therefore, in this work, the performance of different methods for quantifying road condition has been tested with respect to subjective rating at network level. The road condition was quantified using Damage Count (DC), Damaged Area (DA) and Weighted Damage Count (WDC) approaches using algorithms based on YOLOv8 and DeepLabV3 models for detection and segmentation, respectively. An experiment was followed by expert ratings to determine Mean Panel Rating (MPR) for 36 sections on a selected road stretch in Norway. Proposed models to determine Section Condition Rating (SCR) based on the damage count and weighted damage count approaches were able to predict MPR with an R<sup>2</sup> value of 0.93. Further, the SCR<sub>WDC</sub> was found to have a reasonable correlation with International Roughness Index (R<sup>2</sup> equal to 0.79).</p>

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Condition Assessment of Low-Volume Roads in Norway: A Deep Learning Approach for Automatic Assessment at Network Level

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月21日

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  • Madhavendra Sharma,
  • Muneer Al-Hammadi,
  • Helge Mork,
  • Alex Klein-Paste

摘要

Pavement condition monitoring is an important aspect of an efficient Pavement Management System (PMS). However, the monitoring of road sections is a tedious work. Therefore, research communities have come up with deep learning based automatic methods for detecting and classifying road damages. But for a highway authority overlooking a large network of roads, an aggregate measure of road condition is more useful. Therefore, in this work, the performance of different methods for quantifying road condition has been tested with respect to subjective rating at network level. The road condition was quantified using Damage Count (DC), Damaged Area (DA) and Weighted Damage Count (WDC) approaches using algorithms based on YOLOv8 and DeepLabV3 models for detection and segmentation, respectively. An experiment was followed by expert ratings to determine Mean Panel Rating (MPR) for 36 sections on a selected road stretch in Norway. Proposed models to determine Section Condition Rating (SCR) based on the damage count and weighted damage count approaches were able to predict MPR with an R2 value of 0.93. Further, the SCRWDC was found to have a reasonable correlation with International Roughness Index (R2 equal to 0.79).