The Detection Method of Printed Registration Deviations Based on Machine Vision

2013 ◽  
pp. 283-290
Author(s):  
Kailong Liu ◽  
Minrui Fei ◽  
Wenju Zhou ◽  
Haikuan Wang
Keyword(s):  
Machine Vision ◽  
Detection Method

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.

The Appraisal of Velcro’s Surface Quality Based on Machine Vision Systems

2011 ◽  
Vol 201-203 ◽  
pp. 2045-2048
Author(s):  
Da Xing Zhao ◽  
Qing Lin
Keyword(s):  
Machine Vision ◽  
Product Quality ◽  
Surface Quality ◽  
Detection Method ◽  
Experimental Results ◽  
Vision Systems ◽  
Examination Method ◽  
The Common ◽  
Develop Environment ◽  
Common Defect

The most important problem of the Velcro Manufacturer face is to control the surface quality, and how to improve the product quality has become the key of the enterprise. Therefore, this paper take the research on the examination method of the Velcro’s surface flaw, and propose a simply and effectively detection method on the marginal check and the flaw extraction of the buckle in the considering of the system’s real-team and the effectiveness. The experiments have been carried on the results been analyzed under the Visual c + + develop environment. Experimental results show that the system can detect the common defect of the fastening surface accurately and classify them.

Defect Detecting Technology Based on Machine Vision of Industrial Parts

2014 ◽  
Vol 641-642 ◽  
pp. 1275-1279 ◽  
Author(s):  
Xiao Jun He ◽  
Zhen Di Yi ◽  
Jing Liu ◽  
Yu Zheng Wang
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Surface Defects ◽  
Detection Method ◽  
Median Filter ◽  
High Accuracy ◽  
Testing Time

In order to reach and test the surface defects on industrial parts, based on Machine Vision this paper put forward a defective parts detection method. The method of median filter was adopted to eliminate the noise of image. The Ostu-method was used for the segmenting threshold. Pixel level and level edge detection were used to complete the precise components defects detection. Experiments show that this scheme is feasible, and can achieve high accuracy and shorter testing time.

The Assembly Error Detection of Automotive Airbag Based on Machine Vision

2015 ◽  
Vol 738-739 ◽  
pp. 694-698
Author(s):  
Xiao Dong Wang ◽  
Qi Liu ◽  
Wei Zhang
Keyword(s):  
Machine Vision ◽  
Error Detection ◽  
Template Matching ◽  
Assembly Line ◽  
Detection Method ◽  
Ccd Camera ◽  
Processing Method ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Assembly Error

Based on the principle of machine vision technology, we designed a methodto detect the outline dimensions of automotive airbag quickly and accurately. We Used CCD camera obtain the airbag image, through the image processing method ofsmooth filtering andgray-scale transformationto complete pre-processing, finally applied Canny edge detection operator to extract the boundary of the airbag contour features,and then took the template matching methodto detect assemble error of the airbag image whether meet the requirement.The results show that the detection method have a higher precision, and the time is very short, it can improve the sampled positioningerror detection for the all checks image recognition detection, suitable for application in real-time online detection of airbag assembly line.

An Advanced Automatic Remote Meter Detection Method Based on Machine Vision

2014 ◽  
Vol 568-570 ◽  
pp. 994-1000
Author(s):  
Han Li ◽  
Shao Jun Liu ◽  
Ku Wang ◽  
Xia Liu
Keyword(s):  
Risk Assessment ◽  
Machine Vision ◽  
Correlation Coefficient ◽  
Detection Method ◽  
Correlation Coefficients ◽  
Histogram Equalization ◽  
Inspection Robot ◽  
Site Monitoring ◽  
Template Image ◽  
Otsu’S Method

There are a great amount of electronic meters equipped in the distribution substations, which were traditionally monitored by operators. On-site monitoring for risk assessment of these meters is very important. In this paper, we presented an advanced machine vision based automatic meter detection method toward the development of an online automatic meter reading intelligent inspection robot in substation. Firstly, the image received from the inspection robot was enhanced using histogram equalization. Then, the image was segmented into two parts based on the threshold obtained by Otsu’s method. Using these two parts, and the whole enhanced image, circular Hough transformation was applied on these three images and detected the circle with highest probability on them. The normalized correlation coefficients were calculated between the corresponding areas of those three circles from three images and the template image of SF6meter. Finally, the circle with highest correlation coefficient, which was higher than a certain threshold, was determined to be the meter. If it is lower than the threshold, the algorithm would decide that no meter was found in the image. The method was tested with 222 images obtained in one substation in Xi’an, Shanxi, China, and an 87.4% accuracy was achieved using these images, which indicated the potential of this method.

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