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%.

比较了三个预训练卷积神经网络模型（ResNet 50，ResNet 18 和 MobileNet）性能，用于识别路段中的坑洞，并将路面图像分类为小坑洞、大坑洞和正常路面的三个类别。