An enhanced YOLOv8n-seg model with DSConv, SimAM, and GELU reaches 93.7% precision, 90.4% recall, and 93.8% mAP@50 on a new RGB-D pothole dataset, but physical measurements are validated on only five images.
Expert Systems with Applications, 2025
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An Enhanced YOLOv8 Model for Real-Time and Accurate Pothole Detection and Measurement
An enhanced YOLOv8n-seg model with DSConv, SimAM, and GELU reaches 93.7% precision, 90.4% recall, and 93.8% mAP@50 on a new RGB-D pothole dataset, but physical measurements are validated on only five images.