Real-Time Deep Learning Methodology For Pothole Diagnosis

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Abstract

Identifying potholes on roads is extremely important since they can harm people as well as the suspension and wheels of cars. Potholes must be repaired in order to prevent expensive situations when human discomfort is treated and automobile repairs are necessary. Deep learning methods are employed in this work to detect potholes. Using the cameras on smartphones, the input data are gathered as pictures or videos, which are then pre-processed to provide characteristics that aid in the creation of models and prediction. The training data set has a large number of pothole photos. YOLO and CNN are examples of models that are utilized; they are trained using photos of sample potholes so that they can accurately anticipate potholes. Our system will incentivize public servants to locate and fix roads that are broken and contribute to inconvenience and accidents.

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Keywords

CNN; YOLO; Pothole Detection; Deep Learning; Feature Training; Machine Learning; Smartphone Camera