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 Intelligent Point Cloud Edge Detection Method Based on Projection Transformation

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Juan Zhu ◽  
Xiaofeng Yue ◽  
Jipeng Huang ◽  
Zongwei Huang
Keyword(s):  
Edge Detection ◽  
Point Cloud ◽  
Detection Method ◽  
Target Point ◽  
Normal Vector ◽  
Edge Point ◽  
Vertical Projection ◽  
Cloud Data ◽  
Edge Detection Method ◽  
Projection Transformation

An edge detection method based on projection transformation is proposed. First, the vertical projection transformation is carried out on the target point cloud. Data X and data Y are normalized to the width and height of the image, respectively. Data Z is normalized to the range of 0-255, and the depth represents the gray level of the image. Then, the Canny algorithm is used to detect the edge of the projection transformed image, and the detected edge data is back projected to extract the edge point cloud in the point cloud. Evaluate the performance by calculating the normal vector of the edge point cloud. Compared with the normal vector of the whole data point cloud of the target, the normal vector of the edge point cloud can well express the characteristics of the target, and the calculation time is reduced to 10% of the original.

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.

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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