基于YOLO模型的机器人电梯厅门装箱状态快速识别方法

目的针对电梯厅门柔性生产线机器人装箱后厅门状态识别问题,提出一种基于YOLO模型的电梯厅门装箱状态快速识别方法。方法采用工业相机采集装箱后厅门图像信息,并制作成样本训练集,然后将训练集输入到目标识别分类检测模型中,通过调整网络结构参数进行迭代训练。结果经过测试验证,文中提出的识别方法对装箱后厅门的状态分类识别成功率在99%以上,而且识别速度明显优于传统机器视觉处理算法。结论文中提出的厅门装箱状态快速识别方法,可有效解决工业环境中复杂多变光照因素对传统机器视觉处理算法造成的识别效率低、误判率高等问题,并能满足生产系统节拍要求。

Rapid Recognition Method for Loading State of Robot Elevator Hall Door Based on YOLO Model

The work aims to propose a method for rapidly recognizing the state of the elevator hall door based on the YOLO model for the problem of the elevator hall door packing state recognition in the flexible production line robot of elevator hall door. The industrial camera was used to capture the container image and make a sample training set. Then the training set was input into the target recognition classification detection model, and iterative training was performed by adjusting the network structure parameters. After testing and verification, the recognition method proposed had a success rate of more than 99% for hall door state recognition, and the recognition speed was obviously superior to the traditional machine vision processing algorithm. The rapid recognition method for hall door packing state proposed can effectively solve the problems of low recognition efficiency and high misjudgment rate of traditional machine vision processing algorithms due to complex and variable illumination factors in industrial environment, and can meet the beat requirements of production system.