Deep learning and TOPSIS-based multi-criteria decision-making framework for urban road defect detection and sustainable maintenance planning
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View abstract on PubMed
Summary
This summary is machine-generated.This study introduces an improved YOLOv8 model for road defect detection, severity classification, and maintenance prioritization. The new framework enhances urban infrastructure management for safer, sustainable transportation systems.
Area Of Science
- Urban planning and infrastructure management
- Artificial intelligence in transportation
- Sustainable urban development
Background
- Urban road infrastructure maintenance is crucial for quality of life, traffic safety, and sustainability.
- Conventional road flaw detection methods are slow, labor-intensive, and lack real-time capabilities.
- Existing models often fail to assess defect severity or prioritize maintenance, limiting their practical use.
Purpose Of The Study
- To develop an advanced model for simultaneous road defect detection, severity classification, and maintenance prioritization.
- To overcome the limitations of current one-dimensional detection methods for intelligent urban management.
- To enhance decision-making for proactive and sustainable urban infrastructure upkeep.
Main Methods
- An enhanced YOLOv8 model with an additional secondary head for severity classification was proposed.
- Multi-Criteria Decision-Making (MCDM) using the TOPSIS method was integrated.
- The dual-framework model was experimentally validated on a road defect detection dataset.
Main Results
- The improved YOLOv8 model with MCDM demonstrated superior performance compared to the original YOLOv8.
- The framework enables simultaneous detection, severity estimation, and data-driven maintenance prioritization.
- Experimental results confirm the model's viability for both short-term and long-term infrastructure management strategies.
Conclusions
- The proposed dual-framework model represents a significant advancement over existing road defect detection methods.
- It facilitates proactive infrastructure maintenance, enhancing urban mobility resilience.
- The study contributes to creating more secure and sustainable urban transportation systems.
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