Traffic Sign Recognition Based on Convolutional Neural Network Model

Traffic sign recognition (TSR) is a significance research branch in the field of unmanned driving, which is very important for driverless driving and is often used to read permanent or temporary road signs on the roadside. Traffic sign detection (TSD) and traffic sign classification (TSC) constitute a complete recognition system. The paper mainly studies the traffic sign recognition. Traffic sign recognition is mostly applied to portable devices, so the size and detection speed of the model are important factors to be considered. Under the condition of ensuring the speed, the detection accuracy of the model is guaranteed. The accuracy of the model designed in this paper on the German traffic sign recognition benchmark (GTSRB) is 99.30%, the parameter size is only 1.3M, and the trained network model is 4.0M. The results of final experiment show that the network is valid for the classification of traffic signs.