Detection of Wet Road Surfaces from Acoustic Signals using Scalogram and Optimized AlexNet

Shariff, Khairul Khaizi Mohd; Zainuddin, Suraya; Ali, Megat Syahirul Amin Megat

doi:10.1109/iscaie54458.2022.9794556

Cited by 3 publications

(1 citation statement)

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“…Shariff et al [11] employed convolutional NN with transfer learning to avoid the need for supervised learning with a large dataset, achieving above 80% accuracy. In another publication [12], they trained an NN with scalograms calculated from audio data and reached almost 90% accuracy. Bahrami et al [13] used a two-stream convolutional NN for the analysis of acoustic features extracted from the noise recorded inside the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Detection of Water on Road Surface with Acoustic Vector Sensor

Kotus,

Szwoch

2023

Sensors

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This paper presents a new approach to detecting the presence of water on a road surface, employing an acoustic vector sensor. The proposed method is based on sound intensity analysis in the frequency domain. Acoustic events, representing road vehicles, are detected in the sound intensity signals. The direction of the incoming sound is calculated for the individual spectral components of the intensity signal, and the components not originating from the observed road section are discarded. Next, an estimate of the road surface state is calculated from the sound intensity spectrum, and the wet surface detection is performed by comparing the estimate with a threshold. The proposed method was evaluated using sound recordings made in a real-world scenario, and the algorithm results were compared with data from a reference device. The proposed algorithm achieved 89% precision, recall and F1 score, and it outperforms the traditional approach based on sound pressure analysis. The test results confirm that the proposed method may be used for the detection of water on the road surface with acoustic sensors as an element of a smart city monitoring system.

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Section: Introductionmentioning

confidence: 99%