CNN architectures for road surface wetness classification from acoustic signals
The classification of road surface wetness is important for both the development of future driverless vehicles and the development of existing vehicle active safety systems. Wetness on the road surface has an impact on road safety and is one of the leading causes of weather-related accidents. Alth...
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| Format: | Article |
| Language: | English |
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Springer
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| Online Access: | http://psasir.upm.edu.my/id/eprint/104489/ http://psasir.upm.edu.my/id/eprint/104489/1/CNN%20Architectures%20for%20Road%20Surface%20Wetness%20Classification%20from%20Acoustic%20Signals.pdf |
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| author | Bahrami, Siavash Doraisamy, Shyamala Azman, Azreen Nasharuddin, Nurul Amelina Yue, Shigang |
| author_facet | Bahrami, Siavash Doraisamy, Shyamala Azman, Azreen Nasharuddin, Nurul Amelina Yue, Shigang |
| author_sort | Bahrami, Siavash |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | The classification of road surface wetness is important for both the development of future driverless vehicles and the development of existing vehicle active
safety systems. Wetness on the road surface has an impact on road safety and is one
of the leading causes of weather-related accidents. Although machine learning algorithms such as recurrent neural networks (RNN), support vector machines (SVM),
artificial neural networks (ANN) and convolutional neural networks (CNN) have
been studied for road surface wetness classification, the improvement of classification performances are still widely being investigated whilst keeping network and
computational complexity low. In this paper, we propose new CNN architectures
towards further improving classification results of road surface wetness detection
from acoustic signals. Two CNN architectures with differing layouts for its dropout
layers and max-pooling layers have been investigated. The positions and the number
of the max-pooling layers were varied. To avoid overfitting, we used a 50% dropout
layers before the final dense layers with both architectures. The acoustic signals of
tyre to road interaction were recorded via mounted microphones on two distinct
cars in an urban environment. Mel-frequency cepstral coefficients (MFCCs) features
were extracted from the recordings as inputs to the models. Experimentation and
comparative performance evaluations against several neural networks architectures
were performed. Recorded acoustic signals were segmented into equal frames and
thirteen MFCCs were extracted for each frame to train the CNNs. Results show that
the proposed CMCMDD1 architecture achieved the highest accuracy of 96.36% with
the shortest prediction time. |
| first_indexed | 2025-11-15T13:46:42Z |
| format | Article |
| id | upm-104489 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:46:42Z |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1044892023-08-18T03:05:42Z http://psasir.upm.edu.my/id/eprint/104489/ CNN architectures for road surface wetness classification from acoustic signals Bahrami, Siavash Doraisamy, Shyamala Azman, Azreen Nasharuddin, Nurul Amelina Yue, Shigang The classification of road surface wetness is important for both the development of future driverless vehicles and the development of existing vehicle active safety systems. Wetness on the road surface has an impact on road safety and is one of the leading causes of weather-related accidents. Although machine learning algorithms such as recurrent neural networks (RNN), support vector machines (SVM), artificial neural networks (ANN) and convolutional neural networks (CNN) have been studied for road surface wetness classification, the improvement of classification performances are still widely being investigated whilst keeping network and computational complexity low. In this paper, we propose new CNN architectures towards further improving classification results of road surface wetness detection from acoustic signals. Two CNN architectures with differing layouts for its dropout layers and max-pooling layers have been investigated. The positions and the number of the max-pooling layers were varied. To avoid overfitting, we used a 50% dropout layers before the final dense layers with both architectures. The acoustic signals of tyre to road interaction were recorded via mounted microphones on two distinct cars in an urban environment. Mel-frequency cepstral coefficients (MFCCs) features were extracted from the recordings as inputs to the models. Experimentation and comparative performance evaluations against several neural networks architectures were performed. Recorded acoustic signals were segmented into equal frames and thirteen MFCCs were extracted for each frame to train the CNNs. Results show that the proposed CMCMDD1 architecture achieved the highest accuracy of 96.36% with the shortest prediction time. Springer Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/104489/1/CNN%20Architectures%20for%20Road%20Surface%20Wetness%20Classification%20from%20Acoustic%20Signals.pdf Bahrami, Siavash and Doraisamy, Shyamala and Azman, Azreen and Nasharuddin, Nurul Amelina and Yue, Shigang CNN architectures for road surface wetness classification from acoustic signals. Lecture Notes in Electrical Engineering, 835. pp. 777-788. ISSN 1876-1100; ESSN: 1876-1119 https://link.springer.com/chapter/10.1007/978-981-16-8515-6_59 10.1007/978-981-16-8515-6_59 |
| spellingShingle | Bahrami, Siavash Doraisamy, Shyamala Azman, Azreen Nasharuddin, Nurul Amelina Yue, Shigang CNN architectures for road surface wetness classification from acoustic signals |
| title | CNN architectures for road surface wetness classification from acoustic signals |
| title_full | CNN architectures for road surface wetness classification from acoustic signals |
| title_fullStr | CNN architectures for road surface wetness classification from acoustic signals |
| title_full_unstemmed | CNN architectures for road surface wetness classification from acoustic signals |
| title_short | CNN architectures for road surface wetness classification from acoustic signals |
| title_sort | cnn architectures for road surface wetness classification from acoustic signals |
| url | http://psasir.upm.edu.my/id/eprint/104489/ http://psasir.upm.edu.my/id/eprint/104489/ http://psasir.upm.edu.my/id/eprint/104489/ http://psasir.upm.edu.my/id/eprint/104489/1/CNN%20Architectures%20for%20Road%20Surface%20Wetness%20Classification%20from%20Acoustic%20Signals.pdf |