| Summary: | Advancements in computer vision applications have led to improved object detection (OD) in terms of accuracy and processing time, enabling real-time solutions across various fields. In pavement engineering, detecting visual defects such as potholes, cracking, and rutting is of particular interest. This study aims to evaluate YOLO models on a dataset of 665 road pavement images labeled with potholes for OD. Pre-trained deep learning models were customized for pothole detection using transfer learning techniques. The assessed models include You Only Look Once (YOLO) versions 3, 4, and 5. It was found that YOLOv4 achieves the highest mean average precision (mAP), while its shortened version, YOLOv4-tiny, offers the best-reduced inference time, making it ideal for mobile applications. Furthermore, the YOLOv5s model demonstrates potential, attaining good results and standing out for its ease of implementation and scalability.
|
|---|
