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Potholes are fatal and can cause severe damage to vehicles as well as cancause deadly accidents. In South Asian countries, pavement distresses are theprimary cause due to poor subgrade conditions, lack of subsurface drainage, andexcessive rainfalls. The present research compares the performance of threepre-trained Convolutional Neural Network (CNN) models, i.e., ResNet 50, ResNet18, and MobileNet. At first, pavement images are classified to find whetherimages contain potholes, i.e., Potholes or Normal. Secondly, pavements imagesare classi-fied into three categories, i.e., Small Pothole, Large Pothole, andNormal. Pavement images are taken from 3.5 feet (waist height) and 2 feet.MobileNet v2 has an accuracy of 98% for detecting a pothole. The classificationof images taken at the height of 2 feet has an accuracy value of 87.33%,88.67%, and 92% for classifying the large, small, and normal pavement,respectively. Similarly, the classification of the images taken from full ofwaist (FFW) height has an accuracy value of 98.67%, 98.67%, and 100%.

Classification of Potholes Based on Surface Area Using Pre-Trained Models of Convolutional Neural Network