基于双路式卷积神经网络的车辆与行人检测

针对低能见度状态下对车辆与行人的视觉特征难以提取的问题, 提出一种将2路卷积神经网络融合从而实现对车辆与行人识别的方法。采用高斯背景差分法实现图像去模糊, 在双路网络中分别采用不同尺寸的滤波器, 调整滤波器的大小得到不同环境下图片的特征值, 采用反向传播算法计算梯度。实验结果显示, 与单路式卷积神经网络对比, 在能见度低的环境中, 该方法对车辆的辨识率提高至83.49%, 对行人的辨识率提高至87.36%, 表明在低能见度环境中, 双路式卷积神经网络识别准确率高于单路式卷积神经网络。

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.