Vision based Roughness Average Value Detection using YOLOv5 and EasyOCR

Uday Kulkarni, Shashank Agasimani, Pranavi P Kulkarni, Sagar P Kabadi, P. Aditya, Raunak Ujawane
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Abstract

A Rough Surface involves a lot of imperfections and is prone to friction as it offers resistance to moving objects on the surface. The roughness of a Surface is an indicator of the probable performance of every mechanical component since imperfections on the surface might further lead to the formation of nucleation sites for corrosion or ruptures. As rough surfaces have higher friction coefficients as compared to smooth surfaces, it becomes absolutely imperative to test surface roughness and take appropriate action before deployment in automobiles and other industries in order to maintain safety standards. Surface roughness is a calculation of the relative roughness of a surface profile based on a single numeric parameter, Average Roughness (RA). RA is the most commonly specified surface texture parameter measured using a Stylus based instrument wherein a small tip is dragged across any surface while its undulations are recorded which provides a general measure of surface texture in microns. This paper proposes a Machine Learning model developed to read the detected value from the RA Tester and store it in the database thereby reducing manual interference. This proposed model uses a pipeline of the YOLOv5 Algorithm and EasyOCR to detect the Region Of Interest (ROI) from the image and the RA values respectively. This paper produces a real-time solution with an Accuracy of 95.3% for an Automated Entry of the Roughness Average values read directly from the image into the database and has been implemented successfully in the Automobile Industry. This project was conceptualized and Implemented jointly by KLE Technological University and Dana Anand India Private Limited, Dharwad, India.
基于YOLOv5和EasyOCR的视觉粗糙度平均值检测
粗糙的表面包含许多缺陷,并且容易产生摩擦,因为它对表面上移动的物体提供阻力。表面的粗糙度是每个机械部件可能性能的指标,因为表面上的缺陷可能进一步导致腐蚀或破裂的成核位置的形成。由于与光滑表面相比,粗糙表面的摩擦系数更高,因此在汽车和其他行业部署之前,测试表面粗糙度并采取适当措施以保持安全标准是绝对必要的。表面粗糙度是基于单个数值参数平均粗糙度(RA)计算表面轮廓的相对粗糙度。RA是最常用的指定表面纹理参数,使用基于触控笔的仪器测量,其中一个小尖端在任何表面上拖动,同时记录其波动,以微米为单位提供表面纹理的一般测量。本文提出了一种机器学习模型,用于从RA测试仪中读取检测值并将其存储在数据库中,从而减少人工干扰。该模型使用YOLOv5算法和EasyOCR的流水线分别从图像中检测感兴趣区域(ROI)和RA值。本文提出了一种直接从图像中读取粗糙度平均值自动输入数据库的实时解决方案,准确率为95.3%,并已在汽车工业中成功实施。这个项目是由KLE科技大学和印度达尔瓦德的Dana Anand印度私人有限公司共同构思和实施的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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