Zhu Li, Kang Yihua
School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, 430073, CO, China.
School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, CO, China.
Sci Rep. 2025 Jan 6;15(1):1033. doi: 10.1038/s41598-024-83225-6.
Straightness is the basic measurement parameter in machining, and the traditional straightness measurement methods such as light gap method, table method, et al., have extremely low measurement efficiency and cannot achieve online real-time high-precision detection. Our research group has proposed a machine vision online detection based on 10 industrial camera arrays, which can obtain the surface profile straight line of the sucker rod by collecting the edge profile image of the sucker rod and performing morphological transformation. Compared with the traditional method, online nondestructive testing has good real-time performance and high accuracy, but due to the shortcomings of serious environmental pollution and strong noise in some industrial scenarios, the accuracy of the detected straight line is not high. This paper continues to discuss the straightness optimization design method, based on the probability t distribution of the data, reasonably select the sampled edge points, and remove the scene noise. Experiments show that, compared with the previous results, the straightness based on the probability and statistics method can strictly extract the contour sampling points with a confidence of 95%, and the accuracy is higher than that of the traditional Hough transform.
直线度是机械加工中的基本测量参数，传统的直线度测量方法如光隙法、表法等，测量效率极低，无法实现在线实时高精度检测。本研究团队提出了一种基于10个工业相机阵列的机器视觉在线检测方法，通过采集抽油杆边缘轮廓图像并进行形态学变换，可获取抽油杆表面轮廓直线。与传统方法相比，在线无损检测具有良好的实时性和高精度，但由于在某些工业场景中存在环境污染严重、噪声大等缺点，检测直线的精度不高。本文继续探讨直线度优化设计方法，基于数据的概率t分布，合理选择采样边缘点，去除场景噪声。实验表明，与之前的结果相比，基于概率统计方法的直线度能够以95%的置信度严格提取轮廓采样点，精度高于传统霍夫变换。
