Signal analysis for two-axle vehicle weigh-in-motion based on RBF and image processing

The vehicle weight in motion is decided by the weight distribution to every axis. The measurement accuracy is related to the accurate analysis on motion and vibration factors. The radial basis function (RBF) neural network is used to construct the nonlinear model of the weighing system, including the topological structure and the selection of the RBF center. In allusion to the contravention between wide adaptation and imitating accuracy, vehicles are divided into big, medium and small three types. The model is constructed in whole vehicle. The type of the vehicle is achieved by image processing in which the CCD camera is used to get the platform of the vehicle. The different types of vehicle use corresponding neural network. The weight distribution to every axis of vehicle that passes across the bedplate in even speed is also analyzed. The static weight signals are used as the relative real value. Simulation result shows that higher precision of measurement can be achieved as long as the vehicle across the bed-plate in longer time and the type can be achieved.