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An Exploration of Recent Intelligent Image Analysis Techniques for Visual Pavement Surface Condition Assessment
Waqar S Qureshi1, Syed Ibrahim Hassan1, Susan McKeever1
1Department of Computer Science, Technological University Dublin, D07 EWV4 Dublin, Ireland.
Sensors (Basel, Switzerland)
|November 26, 2022
Summary
Automated visual sensing systems can improve pavement condition assessment, but variations in environmental factors and data collection pose challenges. Research is shifting towards deep learning for pavement distress detection, though quantification remains underdeveloped.
Area of Science:
- Civil Engineering
- Computer Science
- Artificial Intelligence
Background:
- Road pavement condition assessment is crucial for infrastructure maintenance, asset management, and budgeting.
- Current assessment methods rely on visual inspections and standardized rating scales, which can be time-consuming and costly.
- Existing surface condition rating systems vary, using different pavement characteristics for evaluation.
Purpose of the Study:
- To critically analyze research trends, professional practices, and commercial solutions for pavement surface condition ratings.
- To identify challenges and opportunities in automated pavement assessment, particularly using intelligent algorithms and deep learning.
- To summarize available datasets and categorize recent academic literature on pavement distress detection and condition assessment.
Main Methods:
- Critical analysis of academic literature, professional practices, and commercial solutions for pavement condition assessment.
- Review of existing surface condition rating systems and their methodologies.
- Categorization of recent research based on datasets, publication details, performance metrics (F1 score), and deep learning architectures.
Main Results:
- Automated visual sensing systems show potential for cost and time reduction in pavement assessment, especially for local and regional roads.
- Significant variations due to environmental factors, pavement types, and image collection devices present challenges.
- Recent academic research increasingly employs deep learning, but often focuses on distress identification rather than quantification.
Conclusions:
- While progress has been made in automated pavement distress detection, a gap exists in distress quantification, which is vital for automated rating systems.
- Further research is needed to address variations and develop robust automated pavement rating systems.
- The development of standardized datasets and methodologies is essential for advancing the field.
