Convolutional neural network for pothole detection in different road and weather conditions

Abstract

Aims: To propose a deep learning algorithm for pothole detection and compare the performance of Sigmoid and Softmax activation functions in the creation of Convolutional Neural Network (CNN) algorithms. Methods: Three different datasets were used to justify the robustness of the CNN model in detecting dry and wet potholes. The CNN algorithms were created separately using the Sigmoid and Softmax activation functions. Results: The CNN algorithm using the Sigmoid function achieved higher accuracy scores than the CNN algorithm using the Softmax function. Specifically, the Sigmoid algorithm achieved accuracy scores of 91%, 96%, and 83% over datasets 1, 2, and 3, respectively, while the Softmax algorithm achieved scores of 81%, 96%, and 85% over the same datasets. Conclusion: The results of this study suggest that the CNN algorithm using the Sigmoid activation function is more robust and effective in detecting pothole images compared to the CNN algorithm using the Softmax activation function.