高铁列车乘务作业视频YOLOv4算法分析应用研究OA

The video recorder on high speed trains records the main process of the crew operation and serves as the primary basis for evaluating work quality standards.Traditional manual analysis of operation videos lacks effective technical support and evaluation management systems,resulting in ineffective supervision and inadequate implementation of crew operation standards.This research proposes a method for analyzing high speed train crew operation videos using a machine learning-based artificial intelligence algorithm,applying the YOLOv4 neural network model.It can automatically identify operation elements inside and outside the train,comprehensively cover the crew duty cycle,and quantify the quality of operations.The system has a mature and stable algorithm structure,meeting the diverse training needs and deployment conditions of various passenger transport sections.It is characterized by flexible configuration,high load capacity,and accurate evaluation.Since its on-site application,the high speed rail fleet in the passenger section has combined operation scenarios,horizontal content classification,and vertical process combing to form a closed loop of operational specifications,making a breakthrough in promoting standardized,professional,and intelligent high speed rail passenger crew management.