Research on Pantograph Safety State Detection Technology Based on Improved YOLOV3 Algorithm
The pantograph is a key component connecting the rolling stock and the contact network,so the safety status of the pantograph is crucial to the smooth and stable operation of the rolling stock.In recent years it has become a mainstream method to detect the safety status of pantograph from images in combination with onboard video monitoring systems.Aiming at the traditional image recognition method's low accuracy and poor real-time performance to detect the pantograph's safety state,the pantograph's safety state is detected by the self-built pantograph safety state data set and the deep learning technique.GAN neural network is used to enhance the data-set to solve the problem of unbalanced data categories;the YOLOV3 algorithm is modified by inserting a plug-and-play attention module to optimize the detection of small targets.Combining with self-researched hardware to realize the analysis of multiple pantographs monitoring real-time video streams provides new ideas and strong support for the real-time intelligent analysis of onboard pantograph video in China's railways.
NETL
NSTL
万方数据
维普
