Machine vision based liquid level inspection system using ISEF edge detection technique

Aiming at the edge detection of thin sheet part dimension inspection system based on machine vision, a contrast research on edge detection is investigated. The Gaussian blurred simulation image and thin sheet part image are took as evaluation images, and the edge detection are done with Roberts operator, Sobel operator, Prewitt operator, Kirsch operator, Laplacian operator, LOG operator and mathematical morphology edge detection method. The results of edge detection are analyzed deeply, and the edge location accuracy, noise resisting ability and calculation time of each algorithm are compared. The single-pixel width connected contour is acquired with mathematical morphology edge detection method, the detection time are 0.0521 second and 0.457 second respectively. It is appropriate that taking the mathematical morphology edge detection method as the edge detection method of thin sheet part dimension inspection system based on machine vision.

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Machine Vision based quality inspection for automotive parts using edge detection technique

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1055/1/012029 ◽

2021 ◽

Vol 1055 (1) ◽

pp. 012029

Author(s):

R Kiruba Shankar ◽

J Indra ◽

R Oviya ◽

A Heeraj ◽

R Ragunathan

Keyword(s):

Machine Vision ◽

Edge Detection ◽

Detection Technique ◽

Quality Inspection ◽

Automotive Parts

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Research on Dimensional Inspection System of Thin Sheet Part Based on Machine Vision

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.2-3.469 ◽

2011 ◽

Vol 2-3 ◽

pp. 469-474

Author(s):

Ji Gang Wu ◽

Xue Jun Li ◽

Bin Qin

Keyword(s):

Machine Vision ◽

Edge Detection ◽

Spline Interpolation ◽

Thin Sheet ◽

Optimization Method ◽

Inspection Time ◽

Inspection System ◽

Adaptive Optimization ◽

Dimensional Inspection ◽

Vision Inspection

Key technologies of dimensional inspection system of thin sheet part based on machine vision are investigated, and an entire machine vision inspection system is developed. A cad information-based line scanning step adaptive optimization method used for image grabbing of inspected part is proposed. A rectangle lens subpixel edge detection method based on cubic spline interpolation used for edge detection is advanced. A planar contour primitive recognition method based on curvature and HOUGH transform used for image recognition is raised. The inspection accuracy of the inspection system can reach to 1μm, and the inspection time can satisfy the requirements of on-line real-time inspection, so the inspection system is feasible.

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Cut-Edge Detection Method for Rice Harvesting Based on Machine Vision

Agronomy ◽

10.3390/agronomy10040590 ◽

2020 ◽

Vol 10 (4) ◽

pp. 590

Author(s):

Zhenqian Zhang ◽

Ruyue Cao ◽

Cheng Peng ◽

Renjie Liu ◽

Yifan Sun ◽

...

Keyword(s):

Machine Vision ◽

Edge Detection ◽

Detection Method ◽

Region Of Interest ◽

Average Error ◽

Vertical Projection ◽

Cut Edge ◽

Fitting Method ◽

Combine Harvester ◽

Edge Detection Method

A cut-edge detection method based on machine vision was developed for obtaining the navigation path of a combine harvester. First, the Cr component in the YCbCr color model was selected as the grayscale feature factor. Then, by detecting the end of the crop row, judging the target demarcation and getting the feature points, the region of interest (ROI) was automatically gained. Subsequently, the vertical projection was applied to reduce the noise. All the points in the ROI were calculated, and a dividing point was found in each row. The hierarchical clustering method was used to extract the outliers. At last, the polynomial fitting method was used to acquire the straight or curved cut-edge. The results gained from the samples showed that the average error for locating the cut-edge was 2.84 cm. The method was capable of providing support for the automatic navigation of a combine harvester.

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