Pothole detection using lightweight network models

Abstract

Potholes are defective cavities found on road surfaces. Potholes can lead to serious accidents and vehicle damage if not properly detected. Thus, we are proposing the use of neural network models for pothole classification. The study involves a comprehensive performance analysis of existing lightweight neural network models in pothole classification, compared against the traditional heavyweight models. Lightweight models are emphasized in the thesis due to their low computational requirements, faster prediction times and better compatibility with real-time detection. We have tested six lightweight models (CCT, CNN, INN, Swin Transformer, EANet and ConvMixer) and four heavyweight models (VGG16. ResNet50, DenseNet201 and Xception). A custom dataset of 900 images containing image samples from roads of Dhaka and Bogura was created by the authors to run the models. The dataset was further augmented into 10,000 images by applying various augmentation methods. Separate tests for each model were conducted in the augmented dataset to compare performance against the original dataset. Augmentation enhanced the performance of 9 out of the 10 models. CNN achieved the highest accuracy of 96.55% and the highest F1 score of 0.96 in our testing. Furthermore, CCT exhibited accuracy of 94.6% and F1 score of 0.9. The lightweight models overall performed better than the heavyweight models in both datasets.