煤矿巡检机器人仪表识别技术的研究

Research on Instrument Recognition Technology of Inspection Robot in Coal Mine

为实现煤矿巡检机器人的无人化和智能化,保障巡检机器人能够读取各类仪表仪器的显示数据,设计了一种基于深度学习的多类型仪表视觉识别系统。该系统利用YOLOv3网络模型进行仪表类型判别以及定位,同时采用改进的径向灰度法结合Hough变换和Canny边缘检测算法来读取指针式仪表的数据;并使用EasyOCR获取数字式仪表的读数。测试结果表明该系统在仪表识别方面有较好的准确性,三类仪表的读数相对误差均小于3.8%。该系统为煤矿巡检机器人的视觉系统提供了一种实际有效的解决方案。

In order to realize the unmanned and intelligent inspection robot in coal mine and ensure that the inspection robot can read the display data of various instruments,a multi-type instrument visual recognition system based on deep learning is designed in this paper.The system uses the YOLOv3 network model to distinguish and locate the instrument type,and uses the improved radial gray method combined with Hough transform and Canny edge detection algorithm to read the data of the pointer instrument.The EasyOCR is used to get the readings of the digital meter.The test results show that the system has good accuracy in instrument identification,and the reading error of the three types of instruments is less than 3.8%.This provides a practical and effective solution for the vision system of inspection robot in coal mine.