Acoustic insights: advancing object classification in urban landscapes using distributed acoustic sensing and convolutional neural networks
Adrián Tomašov, Jan Bukovsky, Pavel Záviška, Tomáš Horváth, Michal Látal, Petr Münster · 2024
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.