Exploring Neural Network Models for Road Classification in Personal Mobility Assistants: A Comparative Study on Accuracy and Computational Efficiency

Gwanghee Lee, Sangjun Moon, Kyoungson Jhang

http://doi.org/10.5626/JOK.2023.50.12.1083

With the increasing use of personal mobility devices, the frequency of traffic accidents has also risen, with most accidents resulting from collisions with cars or pedestrians. Notably, the compliance rate of the traffic rules on the roads is low. Auxiliary systems that recognize and provide information about roads could help reduce the number of accidents. Since road images have distinct material characteristics, models studied in the field of image classification are suitable for application. In this study, we compared the performance of various road image classification models with parameter counts ranging from 2 million to 30 million, enabling the selection of the appropriate model based on the situation. The majority of the models achieved an accuracy of over 95%, with most models surpassing 99% in the top-2 accuracy. Of the models, MobileNet v2 had the fewest parameters while still exhibiting excellent performance and EfficientNet had stable accuracy across all classes, surpassing 90% accuracy.