  Potholes are fatal and can cause severe damage to vehicles as well as can
cause deadly accidents. In South Asian countries, pavement distresses are the
primary cause due to poor subgrade conditions, lack of subsurface drainage, and
excessive rainfalls. The present research compares the performance of three
pre-trained Convolutional Neural Network (CNN) models, i.e., ResNet 50, ResNet
18, and MobileNet. At first, pavement images are classified to find whether
images contain potholes, i.e., Potholes or Normal. Secondly, pavements images
are classi-fied into three categories, i.e., Small Pothole, Large Pothole, and
Normal. 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 classification
of 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 of
waist (FFW) height has an accuracy value of 98.67%, 98.67%, and 100%.
