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%.

翻译：坑洞是致命的，可能导致车辆严重损坏，甚至引发致命事故。在南亚国家，由于路基条件差、缺乏地下排水系统以及过量降雨，路面病害是主要原因。本研究比较了三种预训练卷积神经网络（CNN）模型的性能，即ResNet 50、ResNet 18和MobileNet。首先，对路面图像进行分类以确定是否包含坑洞（即坑洞或正常）。其次，将路面图像分为三类：小坑洞、大坑洞和正常。路面图像取自3.5英尺（腰高）和2英尺高度。MobileNet v2检测坑洞的准确率为98%。在2英尺高度拍摄的图像分类中，大坑洞、小坑洞和正常路面的准确率分别为87.33%、88.67%和92%。类似地，在腰高（FFW）拍摄的图像分类中，准确率分别为98.67%、98.67%和100%。