Research on Visual Detection and Control System of Three Axis Servo Dispensing Machine

Song, Jiao; Wu, Xiao

doi:10.1109/imccc.2018.00120

Cited by 1 publication

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Li et al [22] analyzed the gray feature of fusion hole images (FHIs), and designed an interference rejection algorithm for weld pool images; the geometric size was measured in line with the edge feature of the target FHI. Song and Wu [23] hailed geometric template matching as an ideal method to obtain location geometries of printed board surface, and pointed out the defects of the approach: it is difficult to generalize, and not involved in the improvement of geometric parameter inspection and classification. The above research has been improved in the following aspects:…”

Section: Introductionmentioning

confidence: 99%

Precise Inspection of Geometric Parameters for Polyvinyl Chloride Pipe Section Based on Computer Vision

Bi¹,

Lai²,

Tang³

et al. 2021

View full text Add to dashboard Cite

The precise inspection of geometric parameters is crucial for quality control in the context of Industry 4.0. The current technique of precise inspection depends on the operation of professional personnel, and the measuring accuracy is restricted by the proficiency of operators. To solve the defects, this paper proposes a precise inspection framework for the geometric parameters of polyvinyl chloride (PVC) pipe section (G-PVC), using low-cost visual sensors and high-precision computer vision algorithms. Firstly, a robust imaging system was built to acquire images of a PVC pipe section under irregular illumination changes. Next, an engineering semantic model was established to calculate G-PVC like inner diameter, outer diameter, wall thickness, and roundness. After that, a region-of-interest (ROI) extraction algorithm was combined with an improved edge operator to obtain the coordinates of measured points on PVC end-face image in a stable and precise manner. Finally, our framework was proved highly precise and robust through experiments.

show abstract

Section: Introductionmentioning

confidence: 99%