基于改进 LeNet-5 网络的交通标志识别方法

针对传统LeNet-5卷积神经网络用于交通标志等多种类识别任务中,存在识别正确率低、网络容易过拟合以及梯度消失等问题进行改进。引入Inception卷积模块组来提取目标丰富的特征,同时增加网络的深度。引入BN (batch normalization)层对输入批量样本进行规范化处理;同时改用性能更好的Relu激活函数,并使用全局池化层代替全连接层,合理改变卷积核的大小和数目。研究结果表明,改进LeNet-5网络能够有效解决过拟合和梯度消失等问题,具有较好的鲁棒性;网络识别率达到98. 5%以上,相比CNN (convolutional neural network)+SVM (support vector machine)提高了约5%,比传统的LeNet-5网络提高了3%。可见,改进后的LeNet-5网络图像识别的准确率得到显著提高。

Traffic Sign Recognition Method Based on Improved Lenet-5 Network

For the traditional lenet-5 convolution neural network used in traffic signs and other kinds of recognition tasks, the problems such as low recognition accuracy, easy network over-fitting and gradient disappearance are improved. Inception convolution module group was cited to extract rich features of the target while increasing the depth of the network,Introduce the BN layer to normalize the input batch samples to improve the input of the neural network,At the same time, the better Relu activation function was used, and the global pooling layer was used instead of the full connection layer, and the size and number of convolution kernels were reasonably changed.The research results show that the improved LeNet-5 network can effectively solve the problems of over-fitting and gradient disappearance, and has better robustness.At the same time, compared with CNN+SVM and traditional lenet-5 network, the accuracy of the improved network classification can be up to 98.5%, which is 5% higher than CNN+SVM and 3% higher than traditional lenet-5 network, The accuracy of image recognition is improved significantly.