Vehicle and Pedestrian Detection Method Based on Two-way Convolutional Neural Network

Aiming at the problem that it is difficult to extract the visual characteristics of vehicles and pedestrians under low visibility, a two-way convolution neural network fusion algorithm is proposed to identify vehicle and pedestrian. Using Gauss background difference method to remove fuzziness, and different sizes of filters are used in two-way networks.The size of the filter can be adjusted to get the eigenvalues of the images in different environments, and the backpropagation algorithm is used to calculate the gradient. The experimental results show thatcompared with the single-channel convolution neural network, the recognition rate of the proposed method is increased to 83.49% for vehicles and 87.36% for pedestrians in the low visibility environment, which indicates that the recognition accuracy of the two-way convolution neural network is higher than that of the single-way convolution neural networks.