Vehicle Classification and False Detection Filtering using a Single Magnetic Detector based Intelligent Sensor
Peter Šarčević, Szilveszter Pletl · SZTE Publicatio Repozitórium (University of Szeged) · 2014
Vehicle detection and classification is a very actual problem, because vehicle count and classification data are important inputs for traffic operation, pavement design, transportation planning and other applications.Magnetic detector based sensors provide many advantages compared to other technologies.In this work a new vehicle detection and classification method is presented using a single magnetic detector based system.Due to the relatively big number of false detections caused by vehicles with high metallic content passing in the neighboring lane, a technique for false detection filtering is also presented.Vehicle classes are determined using a feedforward neural network which is implemented in the microcontroller of the detector, together with the detection algorithm and the algorithm used for determining the neural network inputs.The gathering of training samples and testing of the trained neural network have been done in real environment.For the training of the neural network the back-propagation algorithm has been used with different training parameters.