convolutional neural network (CNN) has too many parameters to initialize

and the usual random initialization method is easy to disappear of modified gradient and the problem of premature. The unsupervised PCA learning method is used to obtain oriented initialization parameters. And the gradient descendent method with exponential flexible momentum for updating free parameters of the network is proposed on the basis of analyzing the error propagation of the network. Image detection experiments are respectively carried out on pedestrian detection

and the results show that

compared with other artificial feature detection algorithms

this method can effectively improve target detection accuracy and the detection speed of this method is 20% faster than that of classical CNN; compared with homologous updating mechanism of other momentum

our method has faster convergence and smaller oscillation

and can improve the detection accuracy by 1.6%

1.8% and 6.19% respectively in different depth models.