| 陆兆刚,朱 旋.轧辊磨床屏幕智能相机与图像识别方法研究[J].电子测量与仪器学报,2023,37(7):243-250 |
| 轧辊磨床屏幕智能相机与图像识别方法研究 |
| Research on screen intelligent camera of roller grinder and image recognition method |
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| DOI： |
| 中文关键词: 屏幕数据识别、智能相机 去摩尔纹 畸变校正 |
| 英文关键词:screen data recognition intelligent camera Moire pattern distortion correction |
| 基金项目: |
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| 摘要点击次数: 849 |
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| 中文摘要: |
| 针对轧辊磨床屏幕数据只能依靠人工抄录无法自动获取的问题,设计了一种轧辊磨床屏幕智能相机及轧辊磨床关键参
数自动识别与记录的方法。 将专门设计的小型屏幕智能相机安装于磨床数控屏幕上方,通过 45°倾角“L”型方式设计相机结
构,可以在不影响师傅作业的情况下,对磨床数控屏幕进行拍照。 首先,通过边缘定位和透视变换方法,对获得的屏幕图像进行
配准和矫正;其次,通过训练后的 YOLOv5 模型,对图像中的磨床参数进行识别;最后将磨床关键参数导入数据库,完成轧辊磨
床参数的实时记录和传送,为相关后续生产工序的调整,提供及时、准确的关键设备参数。 另外,针对屏幕图像中普遍存在的摩
尔纹现象,通过设计偏振视窗和去摩尔纹处理算法相结合的方式,有效的滤除摩尔纹,显著降低了摩尔纹对识别精度的影响。
系统运行半年以来,磨床屏幕数据的识别率超过了 99%,显著降低了劳动强度和人工误差,提高了生产率。 |
| 英文摘要: |
| Aiming at the problem that the screen data of roll grinder can only be obtained by manual transcription, we designed a method
of automatic identification and recording of key parameters of roll grinder screen. The specially designed smart camera is installed above
the CNC screen of the grinder, and the camera structure is designed with the angle of 45° “L”, which can take photos of the CNC screen
without affecting the work of the master. Firstly, the screen image is registered and corrected by edge positioning and perspective
transformation. Secondly, grinder parameters in the image were identified by the trained YOLOv5 model. Finally, the key parameters of
the grinder are imported into the database to complete the real-time recording and transmission of the parameters, so as to provide timely
and accurate key equipment parameters for the adjustment of related subsequent production processes. In addition, in view of the
common Moire pattern phenomenon in screen images, the design of polarization window and Moire pattern removal algorithm is combined
to effectively filter Moire pattern, which significantly reduces the influence of Moire pattern on the recognition accuracy. Since the system
has been running for half a year, the recognition rate of grinding machine screen data has exceeded 99%, which significantly reduces
labor intensity and manual error, and improves productivity. |
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