Sucker rod straightness measurement method based on probability statistics of edge point detection - Li Zhu; Yihua Kang (Scientific Reports)
Abstract 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.