为高效识别列车车门的开关状态，并据此控制站台门的同步开关，提出了一种基于轻量级MobileNet网络和机器视觉的图像识别方法，实现高速铁路站台门与列车门的联动控制。在北京南站收集大量列车车门的图像资料，经过预处理后作为模型训练和测试的数据集，再利用二元交叉熵损失函数和Adam优化算法对构建的网络进行训练和优化，最终实现对车门状态的高效精准识别。验证结果表明：对列车开关门动作的识别准确率达到95%以上，识别时间控制在400 ms以内，均能满足当前行业应用需求，极大提高站台门系统的自动化和智能化水平。

To efficiently identify the opening and closing status of train doors and control the synchronous opening and closing of platform doors, a lightweight MobileNet network and machine vision based image recognition method was proposed to achieve linkage control between high-speed railway platform doors and train doors. A large dataset of train door images was collected from Beijing South Station and preprocessed to serve as the training and testing dataset for the model. The constructed network was trained and optimized using a binary cross-entropy loss function and the Adam optimization algorithm to achieve efficient and accurate recognition of door status. Validation results demonstrate an accuracy rate of over 95% in recognizing train door actions, with recognition time kept within 400 milliseconds. These results meet the current industry application requirements and greatly enhance the automation and intelligence level of the platform door system.