Enhancing automatic pothole detection in non-motorized corridors using smartphones: an integrated algorithm

Potholes present a significant safety risk on non-motorized vehicle lanes, especially under low-visibility conditions. Effective pothole detection on non-motorized vehicle lanes is crucial to improve public transportation safety. This study proposes an integrated algorithm that harnesses smartphone sensors to enhance pothole detection accuracy. The algorithm begins with data processing, incorporating techniques such as the quaternion algorithm, synthetic minority over-sampling technique, and wavelet-domain denoising. This preprocessing addresses challenges such as significant smartphone placement uncertainty, limited pothole data, and intense noise signals, all of which severely affect the prediction accuracy of machine learning models. The processed data is subsequently used to train machine learning models for pothole detection, including artificial neural networks (ANNs), bootstrap forest, and Naïve Bayes. The accuracy and precision of the models are evaluated and compared. The results show that the accuracy of pothole detection with the integrated algorithm improved to 92%–97%, surpassing the 70%–90% accuracy reported in previous studies. Using the ANN prediction model, the integrated algorithm achieved the highest overall accuracy of 97.02%, with an F-measure of 95.15%. Additionally, the Naïve Bayes model effectively addresses the class imbalance in pothole detection, achieving the highest precision (97.93%). These results confirm the effectiveness and improved accuracy of the proposed integrated pothole detection algorithm.