Pedestrian detection based on improved LeNet 5 convolutional neural network
In this article, according to the real time and accuracy requirements of advanced vehicle assisted driving in pedestrian detection, an improved LeNet 5 convolutional neural network is proposed. Firstly, the structure of LeNet 5 network model is analyzed, and the structure and parameters of the network are improved and optimized on the basis of this network to get a new LeNet network model, and then it is used to detect pedestrians. Finally, the miss rate of the improved LeNet convolutional neural network is found to be 25% by contrast and analysis. The experiment proves that this method is better than SA Fast R CNN and classical LeNet 5 CNN algorithm.