Research on Edge Detection of Thin Sheet Part Dimension Inspection System Based on Machine Vision

2011 ◽  
Vol 103 ◽  
pp. 194-198
Author(s):  
Ji Gang Wu ◽  
Kuan Fang He ◽  
Bin Qin
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Mathematical Morphology ◽  
Detection Method ◽  
Thin Sheet ◽  
Laplacian Operator ◽  
Inspection System ◽  
Edge Location ◽  
Edge Detection Method ◽  
Part Dimension

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.

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

Agronomy ◽  
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.

scholarly journals Using Improved Edge Detection Method to Detect Mining-Induced Ground Fissures Identified by Unmanned Aerial Vehicle Remote Sensing

2021 ◽  
Vol 13 (18) ◽  
pp. 3652
Author(s):  
Duo Xu ◽  
Yixin Zhao ◽  
Yaodong Jiang ◽  
Cun Zhang ◽  
Bo Sun ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Edge Detection ◽  
Unmanned Aerial Vehicle ◽  
Field Experiments ◽  
High Efficiency ◽  
Detection Method ◽  
Laplacian Operator ◽  
Ground Fissures ◽  
Aerial Vehicle ◽  
Edge Detection Method

Information on the ground fissures induced by coal mining is important to the safety of coal mine production and the management of environment in the mining area. In order to identify these fissures timely and accurately, a new method was proposed in the present paper, which is based on an unmanned aerial vehicle (UAV) equipped with a visible light camera and an infrared camera. According to such equipment, edge detection technology was used to detect mining-induced ground fissures. Field experiments show high efficiency of the UAV in monitoring the mining-induced ground fissures. Furthermore, a reasonable time period between 3:00 a.m. and 5:00 a.m. under the studied conditions helps UAV infrared remote sensing identify fissures preferably. The Roberts operator, Sobel operator, Prewitt operator, Canny operator and Laplacian operator were tested to detect the fissures in the visible image, infrared image and fused image. An improved edge detection method was proposed which based on the Laplacian of Gaussian, Canny and mathematical morphology operators. The peak signal-to-noise rate, effective edge rate, Pratt’s figure of merit and F-measure indicated that the proposed method was superior to the other methods. In addition, the fissures in infrared images at different times can be accurately detected by the proposed method except at 7:00 a.m., 1:00 p.m. and 3:00 p.m.

Research on Feature Edge Detection Method of Large-Size Components Based on Machine Vision

2012 ◽  
Vol 152-154 ◽  
pp. 1367-1372
Author(s):  
Qian Lu ◽  
You Hua Ge ◽  
Zhi Cui
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Detection Method ◽  
Corner Detection ◽  
Image Stitching ◽  
Size Measurement ◽  
Actual Application ◽  
Large Size ◽  
Imaging Model ◽  
Edge Detection Method

Aiming at the problems that existed in the actual application of traditional measurement methods for large-size components, the size measurement and detection method of large-size components based on machine vision was researched and discussed, and the imaging model was built, of which on the basis, the feature edge detection method was applied into the large-size components measurement, thus the image stitching algorithm based on feature edge detection was pointed out, and after simulation, the simulation result shows that the image stitching algorithm based on feature edge detection has good real-time performance compared with the traditional corner detection stitching algorithm, and the former has better accuracy than that of the latter. So the image stitching algorithm based on feature edge detection could enhance the accuracy and efficiency by taking use of large-size components measurement based on machine vision, and it is significative for enhancing the application level of large-size components measurement based on machine vision.

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