| Summary: | Road transport, utilizing roadways, is important for the movement of goods and people, posing a significant maintenance challenge due to the deterioration caused by poor materials and natural disasters. Cracks in asphalt roads are a major concern, impacting transportation quality, safety, and comfort. Identifying these cracks is crucial for maintaining high-quality roads. Nonetheless, the subjective nature of crack detection, time limitations, inaccuracies, and expenses restrict the extent of manual assessment by professionals. To address these issues, this study proposes YOLOv4, a deep learning solution adept at detecting road cracks. Utilizing Unmanned Aerial Vehicles (UAVs) or drones, the road pavement was captured in Perlis, Malaysia, at an altitude of 10 meters for optimal image acquisition. These images are then labelled and input into the YOLOv4 model for training, resulting in accurate crack detection and validation. Comparing the results with the YOLOv3, Faster RCNN, and SSD model, found that YOLOv4 significantly improves crack detection accuracy, especially when using UAV images, thus enhancing previous research outcomes. © 2024 IEEE.
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