| Efficient detection of road quality is critical for the safety and longevity of transportation infrastructure. Traditional methods, including visual inspection, and image processing-based filtering techniques, are time-consuming and often fail to accurately capture the complexity of road damage, such as crack shapes and road widths. Our research critically assesses advanced detection models, including YOLOv5, YOLOv6, YOLOv8, and RT-DETR (Real-Time Detection Transformers), focusing on effective road quality evaluation. |
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