scholarly journals Visual Locating of Reactor in an Industrial Environment Using the Composite Method

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 504
Author(s):  
Chenguang Cao ◽  
Qi Ouyang ◽  
Jiamu Hou ◽  
Liming Zhao
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Region Of Interest ◽  
Corner Detection ◽  
Industrial Environment ◽  
Experimental Systems ◽  
Binocular Stereo ◽  
Image Morphology ◽  
Hole Model ◽  
Set Up

To achieve an automatic unloading of a reactor during the sherardizing process, it is necessary to calculate the pose and position of the reactors in an industrial environment with various amounts of luminance and floating dust. In this study, the defects of classic image processing methods and deep learning methods used for locating the reactors are first analyzed. Next, an improved You Only Look Once(YOLO) model is employed to find the region of interest of the handling hole and a handling hole corner detection method based on the image morphology and a Hough transform is presented. Finally, the position and pose of the reactors will be obtained by establishing a 3D handling hole model according to the principle of a binocular stereo system. To test the performance of the proposed method, a set of experimental systems was set up and experiments were conducted. The results indicate that the proposed location method is effective and the precision of the position recognition can be controlled to within 4.64 mm and 1.68 ° when the cameras are approximately 5 m away from the reactor, meeting the requirements.

Application of Image Processing in Plasma Jet Morphological Analysis

2012 ◽  
Vol 229-231 ◽  
pp. 1136-1139 ◽  
Author(s):  
Xiao Jing Tian ◽  
Hua Jun Dong ◽  
Da Peng Yin ◽  
Zi Yu Zhao
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Morphological Analysis ◽  
Detection Method ◽  
Plasma Jet ◽  
Improved Method ◽  
Forming Quality ◽  
Sobel Operator ◽  
Image Morphology ◽  
Edge Detection Method

The morphology of plasma jet (PJ) directly demonstrates whether the procedure of spray processes is stable. The paper proposes an acquisition system of PJ images and an improved edge detection method is presented to get the morphology of PJ. Firstly, the PJ images are gray enhanced to remove the influence of noises. Then they are enhanced with edge sharpening. At last, they are edge detected through Canny, Laplacian and Sobel operator. From the results we can see that the improved method can get more clear and more complete PJ image morphology than traditional one. The processing methods provide foundation for the online detection of PJ morphology and for diagnosing the forming quality.

scholarly journals Grape Berry Detection and Size Measurement Based on Edge Image Processing and Geometric Morphology

Machines ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 233
Author(s):  
Lufeng Luo ◽  
Wentao Liu ◽  
Qinghua Lu ◽  
Jinhai Wang ◽  
Weichang Wen ◽  
...  
Keyword(s):  
Image Processing ◽  
Detection Method ◽  
Grape Berry ◽  
Corner Detection ◽  
Edge Image ◽  
Grape Berries ◽  
Berry Size ◽  
Average Accuracy ◽  
Different Types ◽  
Geometric Morphology

Counting grape berries and measuring their size can provide accurate data for robot picking behavior decision-making, yield estimation, and quality evaluation. When grapes are picked, there is a strong uncertainty in the external environment and the shape of the grapes. Counting grape berries and measuring berry size are challenging tasks. Computer vision has made a huge breakthrough in this field. Although the detection method of grape berries based on 3D point cloud information relies on scanning equipment to estimate the number and yield of grape berries, the detection method is difficult to generalize. Grape berry detection based on 2D images is an effective method to solve this problem. However, it is difficult for traditional algorithms to accurately measure the berry size and other parameters, and there is still the problem of the low robustness of berry counting. In response to the above problems, we propose a grape berry detection method based on edge image processing and geometric morphology. The edge contour search and the corner detection algorithm are introduced to detect the concave point position of the berry edge contour extracted by the Canny algorithm to obtain the best contour segment. To correctly obtain the edge contour information of each berry and reduce the error grouping of contour segments, this paper proposes an algorithm for combining contour segments based on clustering search strategy and rotation direction determination, which realizes the correct reorganization of the segmented contour segments, to achieve an accurate calculation of the number of berries and an accurate measurement of their size. The experimental results prove that our proposed method has an average accuracy of 87.76% for the detection of the concave points of the edge contours of different types of grapes, which can achieve a good edge contour segmentation. The average accuracy of the detection of the number of grapes berries in this paper is 91.42%, which is 4.75% higher than that of the Hough transform. The average error between the measured berry size and the actual berry size is 2.30 mm, and the maximum error is 5.62 mm, which is within a reasonable range. The results prove that the method proposed in this paper is robust enough to detect different types of grape berries.

Design of Soil-Granularity Analysis System Based on MATLAB

2015 ◽  
Vol 740 ◽  
pp. 722-726 ◽  
Author(s):  
Jian Lin Rao ◽  
Jian Shu Hou ◽  
Hao Chen ◽  
Hai Hua Li ◽  
Xue Yi Wan ◽  
...  
Keyword(s):  
Image Processing ◽  
Grain Size ◽  
Extraction Method ◽  
Detection Method ◽  
Region Of Interest ◽  
Edge Extraction ◽  
New Type ◽  
Image Edge ◽  
Analysis System ◽  
Soil Grain Size

The system in the paper based on Matlab platform. With the aid of image processing toolbox of soil image analysis and processing, the soil grain size distribution and its inclination angle can be got. It overcomes the insufficiency of the existing image edge extraction method, and proposes a new type of detection method, in order that the region of interest can be more accurately extract. The system is helpful to predict the possibility of the regional landslides.

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.

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

IMAGE PROCESSING VIA CNN GENES WITH VON NEUMANN NEIGHBORHOODS

2005 ◽  
Vol 15 (12) ◽  
pp. 3999-4006 ◽  
Author(s):  
FENG-JUAN CHEN ◽  
FANG-YUE CHEN ◽  
GUO-LONG HE
Keyword(s):  
Image Processing ◽  
Computer Simulation ◽  
Edge Detection ◽  
Vertical Line ◽  
Corner Detection ◽  
Line Detection ◽  
Von Neumann ◽  
Center Point ◽  
Better Than

Some image processing research are restudied via CNN genes with five variables, and this include edge detection, corner detection, center point extraction and horizontal-vertical line detection. Although they were implemented with nine variables, the results of computer simulation show that the effect with five variables is identical to or better than that with nine variables.

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