An Yuan, Yang Chunyu, Zhang Shuo
China University of Mining and Technology, Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, Xuzhou, China.
Xuzhou University of Technology, Jiangsu Province Key Laboratory of Intelligent Industry Control Technology, Xuzhou, China.
Front Neurosci. 2024 Jun 18;18:1431033. doi: 10.3389/fnins.2024.1431033. eCollection 2024.
As an important part of the unmanned driving system, the detection and recognition of traffic sign need to have the characteristics of excellent recognition accuracy, fast execution speed and easy deployment. Researchers have applied the techniques of machine learning, deep learning and image processing to traffic sign recognition successfully. Considering the hardware conditions of the terminal equipment in the unmanned driving system, in this research work, the goal was to achieve a convolutional neural network (CNN) architecture that is lightweight and easily implemented for an embedded application and with excellent recognition accuracy and execution speed. As a classical CNN architecture, LeNet-5 network model was chosen to be improved, including image preprocessing, improving spatial pool convolutional neural network, optimizing neurons, optimizing activation function, etc. The test experiment of the improved network architecture was carried out on German Traffic Sign Recognition Benchmark (GTSRB) database. The experimental results show that the improved network architecture can obtain higher recognition accuracy in a short interference time, and the algorithm loss is significantly reduced with the progress of training. At the same time, compared with other lightweight network models, this network architecture gives a good recognition result, with a recognition accuracy of 97.53%. The network structure is simple, the algorithm complexity is low, and it is suitable for all kinds of terminal equipment, which can have a wider application in unmanned driving system.
作为无人驾驶系统的重要组成部分，交通标志的检测与识别需要具备识别准确率高、执行速度快、易于部署的特点。研究人员已成功将机器学习、深度学习和图像处理技术应用于交通标志识别。考虑到无人驾驶系统中终端设备的硬件条件，在本研究工作中，目标是实现一种卷积神经网络（CNN）架构，该架构轻量级且易于在嵌入式应用中实现，同时具有出色的识别准确率和执行速度。作为一种经典的CNN架构，选择了LeNet-5网络模型进行改进，包括图像预处理、改进空间池卷积神经网络、优化神经元、优化激活函数等。在德国交通标志识别基准（GTSRB）数据库上对改进后的网络架构进行了测试实验。实验结果表明，改进后的网络架构在短干扰时间内能够获得更高的识别准确率，并且随着训练的进行算法损失显著降低。同时，与其他轻量级网络模型相比，该网络架构给出了良好的识别结果，识别准确率为97.53%。网络结构简单，算法复杂度低，适用于各类终端设备，在无人驾驶系统中具有更广泛的应用。
