A Convolutional Neural Network-Based Method for Small Traffic Sign Detection

Abstract:

In order to solve the small target detection convolutional neural network algorithm, the PVANet convolutional neural network structure was improved to conduct the experiments of traffic sign detection on the TT100K traffic sign data set. The shallow feature extraction, deep feature extraction, and HyperNet multilayer feature fusion modules were improved. The experimental results show that the improved PVANet convolutional neural network has an excellent detection ability for small target objects, and the corresponding traffic sign detection algorithm can achieve a higher mAP (mean average precision).