Wet road detection using CNN with transfer learning

Mohd Shariff, Khairul Khaizi and MD Ali, Mohd Adli and Enche Ab Rahim, Siti Amlina and Khan Ismail, Zuhani (2022) Wet road detection using CNN with transfer learning. In: 2022 IEEE 12th Symposium on Computer Applications & Industrial Electronics, 21-22 Jul 2022, Penang, Malaysia.

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Abstract

here is an increasing need to detect wet road surfaces automatically considering many accidents and traffic problems that occur in wet weather. Road condition detection based on acoustic signals has gained more attention in recent years due to its low implementation cost. However, current deep learning methods for wet surface detection rely on supervised audio measurements. Furthermore, they require a large amount of training data. Recent advancements in convolutional neural networks (CNNs) have made it possible for transferring trained CNN from one dataset to another. In this study, we aim to evaluate the capabilities of pre-trained CNN models to detect wet road surfaces. Results show that transfer learning was able to discriminate between dry and wet road surfaces with an accuracy of more than 80%. Additionally, we also provide performance comparisons for the three trained models.

Item Type:	Conference or Workshop Item (Slide Presentation)
Uncontrolled Keywords:	Wet Road detection, Machine learning, Convolutional neural network, Transfer learning, Scalogram
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Science Kulliyyah of Science > Department of Physics
Depositing User:	Dr Mohd Adli MD Ali
Date Deposited:	21 Jul 2022 11:28
Last Modified:	21 Jul 2022 11:28
URI:	http://irep.iium.edu.my/id/eprint/98861

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