基于卷积神经网络的地铁保护区风险源识别北大核心CSCD

城市轨道交通沿线的风险源识别是防止违规作业导致的安全事故的重要手段。为解决传统识别方法效率低、漏检率高、成本大等问题,本文基于无人机地铁保护区巡检系统,采用卷积神经网络对无人机采集的影像数据进行风险源识别。首先介绍无人机影像获取的流程,并在原始影像数据的基础上,通过数据增强的方式制作多角度、多尺度的风险源数据集;然后使用卷积神经网络建立风险源识别模型,对无人机采集影像中的风险源进行自动识别和定位。试验结果表明,多角度、多尺度风险源数据集的建立进一步提升了模型的识别准确率,且比传统方法具有效率高、成本低等优点。

Risk source identification in metro protection area based on convolutional neural network

The identification of risk sources along urban rail transit is an important method to prevent safety accidents caused by illegal operations. In order to solve the problems of low efficiency, high miss rate and high cost of traditional identification methods, this paper takes the UAV metro protection area inspection system as a platform, and uses convolutional neural network to identify the risk source of the image data collected by UAV. Firstly, the process of UAV image acquisition is introduced, and based on the original image data, multi-angle and multi-scale of risk source datasets are produced through data enhancement. Then, a method of convolutional neural network is used to establish risk source identification models to automatically identify and locate the risk source in the images collected by the UAV. Experiments show that the establishment of multi-angle and multi-scale risk source data sets further improves the recognition accuracy of the model, and this method has the advantages of high efficiency and low cost compared with traditional methods.