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

TL;DR

This study evaluates the effectiveness of pre-trained CNN models, including ResNet and MobileNet, in classifying pavement images to detect and categorize potholes with high accuracy, aiding in road maintenance efforts.

Contribution

The paper compares multiple pre-trained CNN models for pothole detection and classification, demonstrating high accuracy in real-world pavement image analysis.

Findings

MobileNet v2 achieves 98% accuracy in pothole detection.

High classification accuracy for small, large, and normal potholes at different image heights.

ResNet models also show strong performance in pavement image classification.

Abstract

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%.