Pothole Detection Using Transfer Learning Models: A Comparative Study

In this fast-paced modern world potholes are considered as some random holes on the surface of the roads and are considered as mere obstacles while traveling. But reality is much harsher than these considerations as these mere potholes are solely responsible for a significant amount of road accidents which involve hundreds of deaths and much higher property damages. This is a detailed comparative study of some popular deep-learning algorithms. The main objective of this comparative study is to find a better solution to tackle the pothole problem faced by the countries whose economy is based mostly on transport systems. The base model of these algorithms is tweaked to bring out their best results on the used dataset. Results are decided based on the output accuracy delivered by the respective algorithms. These algorithms include CNN, VGG19, VGG16, InceptionResNetV2, InceptionV3, MobileNetV2, and Xception. The MobileNetV2 with layer freezing has emerged as the best of all models used in this study with an accuracy of 96.37%. It has also taken the least computational time for each image.