Acoustic insights: advancing object classification in urban landscapes using distributed acoustic sensing and convolutional neural networks

Adrian Tomasov; Jan Bukovsky; Pavel Zaviska; Tomas Horvath; Michal Latal; Petr Munster

doi:10.1117/12.3021990

18 June 2024 Acoustic insights: advancing object classification in urban landscapes using distributed acoustic sensing and convolutional neural networks

Adrian Tomasov, Jan Bukovsky, Pavel Zaviska, Tomas Horvath, Michal Latal, Petr Munster

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Proceedings Volume 13017, Machine Learning in Photonics; 1301715 (2024) https://doi.org/10.1117/12.3021990
Event: SPIE Photonics Europe, 2024, Strasbourg, France

Conference Poster

Abstract

The paper introduces an innovative object classification method for urban environments, employing distributed acoustic sensing (DAS) to address the complexities of urban landscapes. Utilizing omnipresent optical telecommunication cables, our approach involves a modified convolutional neural network (CNN) with transfer learning, achieving up to 85% accuracy. This method reuses most of the original network for feature extraction, with a final layer customized for new urban datasets – initially trained at the Brno University of Technology and then adapted to city center data. The model effectively identifies urban elements like vehicles and pedestrians, showcasing the potential of DAS for real-time classification in urban management and planning.

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Adrian Tomasov, Jan Bukovsky, Pavel Zaviska, Tomas Horvath, Michal Latal, and Petr Munster "Acoustic insights: advancing object classification in urban landscapes using distributed acoustic sensing and convolutional neural networks", Proc. SPIE 13017, Machine Learning in Photonics, 1301715 (18 June 2024); https://doi.org/10.1117/12.3021990

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