Signal Processing of Distributed Optoacoustic Sensors by Means of Neural Networks in the Automotive Transport Monitoring Problem

Artificial neural network (ANN) models are used as a tool for an automotive transport monitoring. The solution of the problem of recognition of distributed optoacoustic sensor signals generated by vehicles using ANNs is considered. Signals features and signals preliminary processing are described. The ANN architecture for the vehicles generated signals recognition is selected. The architecture of the ANN of vehicles signal recognition, including heavy tracks signals, has single layer with two hundred and one input and one or two outputs. The ANN is designed using the Python programming language, Scikit-Learn, Keras and NumPy libraries. The ANN training patterns, the training results and the trained ANN practical application are described. The recommendations for further research in the field of using ANNs of various architectures for recognizing vehicle signals using distributed optoacoustic sensors are given. The study results are important for road traffic monitoring, as well as other areas of the distributed optoacoustic sensor applications.