Vehicle classification algorithm based on binary proximity sensors and neural networks - ZHANG Wei; TAN Guo-zhen; DING Nan; SHANG Yao (Tongxin xuebao)
To improve the classification accuracy, a new algorithm was developed with binary proximity magnetic sen- sors and back propagation neural networks. In this algorithm, use the low cost and high sensitive magnetic sensors to de- tect the magnetic field distortion when vehicle pass by them and estimate vehicle length with the geometrical characteris- tics of binary proximity networks, and finally classify vehicles via neural networks. The inputs to the neural networks in- clude the vehicle length, velocity and the sequence of features vector set, and the output is predefined vehicle types. Simulation and on-road experiment obtains high recognition rate of 93.61%. It verified that this algorithm enhances the vehicle classification with high accuracy and solid robustness.