卷积神经网络(CNN)在辅助交通标志识别中的应用

Application of convolutional neural network(CNN)in recognition of traffic signs

交通标志的辨识在无人驾驶以及辅助驾驶当中具有重要的应用,交通标志识别算法性能的好坏,会直接影响到车辆在交通系统中的行驶安全。文章首先阐述了卷积神经网络(CNN)技术的实现原理,之后详细介绍了利用卷积神经网络技术对交通标志进行识别的方法,包括了具体的步骤以及交通标志的识别结果等,对于提高交通标志的识别效果以及推动智能交通的发展具有重要的意义和价值。

Traffic sign recognition has important applications in driverless and assisted driving. The performance of the traffic sign recognition algorithm will directly affect the safety of vehicles in the traffic system. This paper first expounds the realization principle of convolutional neural network technology, and then introduces in detail the method of using convolutional neural network technology to recognize traffic signs, including the specific steps and the recognition results of traffic signs, etc. In order to improve the recognition of traffic signs. It is a great significance and value to promote the development of intelligent transportation.