基于机器视觉的扶梯自动急停系统

针对自动扶梯上乘客易摔倒的安全隐患,设计一款基于机器视觉的人体摔倒行为识别系统及扶梯自动急停装置.借助OpenPose人体关节点检测算法提取目标人体的骨骼特征,利用Inception V3网络模型搭建分类器,对采集的骨骼特征信息分类,以识别乘客摔倒行为.训练结果表明单人、多人样本的测试精度最高可达98.9%、80.0%.识别摔倒行为后将检测结果以无线通讯的方式发送至基于STM32微控制器及多种传感器的急停装置.最后,在模拟的扶梯环境下进行实验测试,测试结果表明该扶梯自动急停系统的控制实时性良好.

Escalator Automatic Emergency Stop System Based on Machine Vision

In view of the safety hazard of passengers falling down on the escalator, a machine-based human fall behavior recognition system and an escalator automatic emergency stop device were designed. The OpenPose human joint detection algorithm is used to extract the bone characteristics of the human body. The Inception V3 network model is used to build the classifier, and the collected bone feature information is classified to identify the fall behavior of passenger. The training results show that the test accuracy of single and multi-person samples is up to 98.9% and 80.0%. After the fall behavior is identified, the test results are wirelessly transmitted to the emergency stop device based on the STM32 microcontroller and various sensors. Finally, the experimental test is carried out in the simulated escalator environment. The test results show that the control of the escalator automatic emergency stop system has good real-time performance.