Road Crack Detection using Deep Learning

The road is a path that helps to connect various locations. It is essential to our day-to-day existence. The state of the weather conditions, overloading, poor maintenance, and a few other factors all contribute to road distress. The prevalent stressors are cracks, rutting, and potholes. Accidents on the roads have increased recently as a result of poor upkeep. Safety and road upkeep are important issues for transportation infrastructure. Road cracking is the fundamental and underlying source of other damages like potholes, etc., so manually identifying this distress requires labor-intensive, human inspection. Among the many difficulties, seeing fractures in the road ahead of time is crucial to avoiding collisions and expensive repairs. In this study, we provide an automated deep learning framework technique for spotting road cracks. The suggested method is trained using road photo datasets from many sources, which include a range of road surface types, lighting situations, and fracture severity. After that, data augmentation approaches are utilized to enhance model generalization. We use a variety of deep learning frameworks and fine-tune the hyper parameters to further enhance the model's performance and Reliability and consistency in grading outcomes are provided by the proposed deep learning approach, which is also computationally less expensive. The recommended deep learning method shows amazing accuracy in identifying and categorizing potholes, making it a useful instrument for road maintenance agencies.