Road Shoulder Classification Using the CNN Algorithm with the MobileNetV2 Model

DOI

10.2991/978-94-6463-364-1_24How to use a DOI?

Keywords

CNN; MobileNet V2; Road Shoulder Surface

Abstract

The condition of the road that is traversed by high and repeated traffic volumes will affect the condition of road construction, leading to a decline in the quality of the road, which impacts the safety, comfort, and smoothness of traffic. This paper will discuss Deep learning to evaluate damage detection models and classify large- scale road shoulder surface data sets in advanced Convolutional Neural Network (CNN) algorithms. One of the models that maintain high accuracy and produce better results is Mobilenet V2. From the MobileNet V2 model, it has been confirmed to obtain the best accuracy results for each epoch and batch size for shoulder parameter classification are an accuracy rate of approximately 0.7 to 0.8 with a minimum loss value of 0.2 to 0.3; thus, using the MobileNet V2 model to classify the shoulder yields the optimal results.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Rahardhita Sudibyo
AU  - Haniah Mahmudah
PY  - 2024
DA  - 2024/02/17
TI  - Road Shoulder Classification Using the CNN Algorithm with the MobileNetV2 Model
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
PB  - Atlantis Press
SP  - 246
EP  - 257
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-364-1_24
DO  - 10.2991/978-94-6463-364-1_24
ID  - Sudibyo2024
ER  -