scholarly journals Development of a machine vision system for rice seed inspection system

Food Research ◽  
2020 ◽  
Vol 4 (S6) ◽  
pp. 150-156
Author(s):  
R. Ruslan ◽  
S. Khairunniza-Bejo ◽  
I.H. Rukunudin ◽  
M. Jahari ◽  
M. Ibrahim
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Vision System ◽  
Image Acquisition ◽  
Inspection System ◽  
Weedy Rice ◽  
Rice Seed ◽  
Cultivated Rice ◽  
Purity Analysis ◽  
The Government

Rice seed production in Malaysia is greatly dependent on the purity of the cultivated paddy seed produced through the government certified paddy seed program. The seeds to be marketed by the seed processors must undergo quality control protocol where the seed lots are sampled from the seed farms and seed processing plants for purity analysis by the enforcing agency at the Seed Testing Laboratory of the Department of Agriculture (DoA). The current inspection conducted by the laboratory is based on a manual process, which is laborious and time-consuming. Therefore, a prototype (Patent ID: PI2018500018) of a machine vision-based rice seed inspection system (RiSe-IViS) was developed to explore the possibility of replacing the existing manual method in distinguishing the weedy rice and cultivated rice seeds under the Standard Jabatan Pertanian Malaysia (SJPM) standard protocol with a modern, effective and efficient technique using an image processing approach. The developed RiSe-IViS prototype consists of two parts i) hardware configuration and ii) software development. This paper discussed the criteria to be established, challenges and limitation encountered in developing the hardware prototype involving the image acquisition setup, lighting configuration and seed plate design. The importance of each criterion to ensure its reproducibility are also discussed. A software programme was developed to assist the user for image acquisition and analysis. The image processing steps undertaken in the programme are also discussed. The RiSe-IViS is expected to classify major rice seed varieties available in Malaysia against the weedy rice variants with superior accuracy.

scholarly journals An Automatic Machine Vision-Guided System for the Propagation of Potato Test-Tube Plantlets

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Xu Shengyong ◽  
Peng Biye ◽  
Wu Haiyang ◽  
Li Fushuai ◽  
Cai Xingkui ◽  
...  
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Vision System ◽  
Image Acquisition ◽  
Processing System ◽  
Test Tube ◽  
Automatic Machine ◽  
Node Point ◽  
Acquisition Device ◽  
Image Acquisition Device

In manually propagating potato test-tube plantlets (PTTPs), the plantlet is usually grasped and cut at the node point between the cotyledon and stem, which is hardly located and is easily damaged by the gripper. Using an agricultural intelligent robot to replace manual operation will greatly improve the efficiency and quality of the propagation of PTTPs. An automatic machine vision-guided system for the propagation of PTTPs was developed and tested. In this paper, the workflow of the visual system was designed and the image acquisition device was made. Furthermore, the image processing algorithm was then integrated with the image acquisition device in order to construct an automatic PTTP propagation vision system. An image processing system for locating a node point was employed to determine a suitable operation point on the stem. A binocular stereo vision algorithm was applied to compute the 3D coordinates of node points. Finally, the kinematics equation of the three-axis parallel manipulator was established, and the three-dimensional coordinates of the nodes were transformed into the corresponding parameters X, Y, and Z of the three driving sliders of the manipulator. The experimental results indicated that the automatic vision system had a success rate of 98.4%, 0.68 s time consumed per 3 plants, and approximate 1 mm location error in locating the plantlets in an appropriate position for the medial expansion period (22 days).

Reconfigurable Image Acquisition in the Machine Vision Inspection System

2012 ◽  
Vol 197 ◽  
pp. 376-380
Author(s):  
Da Xing Zhao ◽  
Lei Peng ◽  
Guo Dong Sun ◽  
Wei Feng
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Image Matching ◽  
Visual Inspection ◽  
Detection System ◽  
Image Acquisition ◽  
Inspection System ◽  
Image Detection ◽  
Vision Inspection ◽  
Machine Vision Inspection

Since camera drivers provided by the different manufacturers are not compatible, machine vision systems must be redeveloped according to specific camera. It is great significant to work out the problem, which could improve the versatility of the inspection system. The reconfigurable technology has applied to image processing, image matching and so on. Hence, in the paper the reconfigurable image acquisition module is designed, which reserves some interfaces for the image detection module. Citing the nonel visual inspection system as an example, adopting DALSA and BASLER cameras to acquire the images, the images was displayed properly. Therefore, the compatibility of the image detection system has been improved greatly.

Intelligent Vision Inspection System for IC Engine Head: An ANN Approach

2012 ◽  
Vol 479-481 ◽  
pp. 2242-2245 ◽  
Author(s):  
Rajesh Kanna ◽  
Manikandan Saravana
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Back Propagation ◽  
Engine Block ◽  
Inspection System ◽  
Back Propagation Algorithm ◽  
Ic Engine ◽  
Vision Inspection ◽  
System Module ◽  
Artificial Neural Network Ann

A machine vision system based on Artificial Neural Network (ANN) for inspection of IC Engine block was developed to identify the misalignment and improper diminishing of holes in the IC Engine block. The developed machine vision and ANN module is compared with the commercial MATLAB® software and found results were satisfactory. This work is broadly divided into four stages, namely Intelligent inspection module, Machine Vision module, ANN module and Expert system module. A system with a camera was used to capture the various segments of head of the IC Engine block. The captured bitmap format image of IC Engine block has to be filtered to remove the noises present while capturing and the size is also altered using SPIHT method to an acceptable size and will be given as input to ANN. Generalized ANN with Back-propagation algorithm was used to inspect the IC Engine block. ANN has to be trained to provide the inspected report.

Study on Image Processing of Wheel Valve Hole Based on Machine Vision

2011 ◽  
Vol 63-64 ◽  
pp. 541-546 ◽  
Author(s):  
Chang Chun Li ◽  
Shi Feng Wang ◽  
Jing Yu ◽  
Hua Guan Liu
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Machine Vision ◽  
Edge Detection ◽  
Basic Principle ◽  
Image Acquisition ◽  
Wheel Speed ◽  
Hole Edge

This paper discusses the basic principle for automatic searching the wheel valve hole based on machine vision. Image acquisition and image processing have been done, and we analyzed the factors that impact the image quality of wheel valve hole. This paper argues that many parameters such as the wheel speed, painting color, the distance between the camera and the valve hole, edge detection operator, and they will affect the quality of the image acquisition and image processing of valve hole.

Automatic Inspection System of Small Plastic Gears Based on Machine Vision

2012 ◽  
Vol 522 ◽  
pp. 628-633 ◽  
Author(s):  
Jian Zhe Chen ◽  
Gui Tang Wang ◽  
Jian Qiang Chen ◽  
Xin Liang Yin
Keyword(s):  
Machine Vision ◽  
Template Matching ◽  
Vision System ◽  
Inspection System ◽  
Injection Molding Process ◽  
Automatic Inspection ◽  
Machine Vision System ◽  
Molding Process ◽  
Small Plastic ◽  
Plastic Gears

Small plastic gear is generally made by injection molding.But the injection molding process and mold have problems with missing tooth, shrink, more material, less material and inaccurate roundness and so on. Furthermore, using manual inspection will appear phenomenon of low efficiency, false detection and leak detection. To solve these problems, this paper introduces an automatic inspection system of small plastic gears based on machine vision. The system consists of feeding and sorting machine control system and machine vision inspection system of the gear defects. Mechanical control use digital servo control technology to achieve automatic nesting, feeding, positioning of gear workpiece, and depend on the inspection result of machine vision system to sort. After acquired gear image through a camera, Machine vision system uses median filtering, binarization, edge detection algorithms to process image. Then the system adopts template matching algorithm to obtain the inspection result and send the result to the sorting controller, which achieve automatic smart inspection of gear. The automatic inspection system has accurate, efficient, intelligent and other advantages.

Machine Vision System for Inspecting Surgical Blades

2010 ◽  
Vol 139-141 ◽  
pp. 2067-2071
Author(s):  
Shu Yi Wang ◽  
Jie Tan ◽  
Xun Chao Yin ◽  
Hui Fang Wang
Keyword(s):  
Machine Vision ◽  
Template Matching ◽  
Precise Measurement ◽  
Vision System ◽  
Inspection System ◽  
Measuring Range ◽  
One Dimensional ◽  
Maximum Range ◽  
Matching Technique ◽  
Telecentric Lens

In this paper, a surgical blade inspecting system is designed and researched, which can automatic test and determine eligibility according to tolerances. High precision and non-contact detection of surgical blades based on machine vision is realized by using camera with telecentric lens to obtain the image of surgical blades, by applying template matching technique to automatic match surgical blades. One-dimensional edge of sub-pixel accuracy is extracted by combining the edge filtering of the Deriche filter and parabolic fitting for the maximum range from edge. It can be used for precise measurement of different part of surgical blades with different sizes. Extensive experiments showed that measuring range is from 2mm ~ 50mm, measuring accuracy is 10μm and average detection time is 0.6s. Thus the inspection system is fast, accurate and robust to fulfill the industrial demands.

Study on the Positioning Error of Turntable Based on Machine Vision System

2014 ◽  
Vol 530-531 ◽  
pp. 467-471
Author(s):  
Fu Sheng Yu ◽  
Zhong Guo Sun ◽  
Sheng Jiang Yin ◽  
Teng Fei Li ◽  
Wei Kang Shi
Keyword(s):  
Machine Vision ◽  
Vision System ◽  
Image Acquisition ◽  
Vertical Direction ◽  
Calibration Method ◽  
Positioning Error ◽  
Positioning Errors ◽  
Data Processing Software ◽  
Accuracy Measure ◽  
Edge Detection Method

This paper developed a turntable positioning error measurement system based on machine vision. The system consists of image acquisition devices, the image acquisition card, computer and data processing software and other components. Among them, the image acquisition devices consisted of two digital CCD cameras and two microscope objectives. The image acquisition devices capture images of fixture fixed on the turntable in horizontal and vertical direction. Then, the collected images are processed by adopting the filtering method, binarization method, edge detection method, calibration method and other steps. The high-accuracy measure of turntables positioning errors is realized, and the error histogram is drawn. Theoretical analysis and experimental results show that the method is correct and feasible.

scholarly journals Measurements of surface flatness distortions using machine vision inspection system

Mechanik ◽  
2017 ◽  
Vol 90 (12) ◽  
pp. 1155-1156
Author(s):  
Anna Zawada-Tomkiewicz ◽  
Dariusz Tomkiewicz ◽  
Lesław Wilk
Keyword(s):  
Thermal Treatment ◽  
Machine Vision ◽  
Vision System ◽  
Float Glass ◽  
Inspection System ◽  
Vision Inspection ◽  
Surface Flatness ◽  
Machine Vision Inspection

The use of a vision system for evaluating the flatness distortion of float glass under thermal treatment in a horizontal process is presented. The possibility of evaluation of such parameters as overall bow, roller wave and edge lift was analyzed for a pane of glass taken from production.

scholarly journals Wear and Breakage Detection of Integral Spiral End Milling Cutters Based on Machine Vision

Materials ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 5690
Author(s):  
Wenming Wei ◽  
Jia Yin ◽  
Jun Zhang ◽  
Huijie Zhang ◽  
Zhuangzhuang Lu
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
End Milling ◽  
Image Acquisition ◽  
Acquisition System ◽  
Moving Frame ◽  
Light Sources ◽  
Complex Structures ◽  
Processing Methods ◽  
Image Acquisition System

Tool wear and breakage detection technologies are of vital importance for the development of automatic machining systems and improvement in machining quality and efficiency. The monitoring of integral spiral end milling cutters, however, has rarely been investigated due to their complex structures. In this paper, an image acquisition system and image processing methods are developed for the wear and breakage detection of milling cutters based on machine vision. The image acquisition system is composed of three light sources and two cameras mounted on a moving frame, which renders the system applicable in cutters of different dimensions and shapes. The images captured by the acquisition system are then preprocessed with denoising and contrast enhancing operations. The failure regions on the rake face, flank face and tool tip of the cutter are extracted with the Otsu thresholding method and the Markov Random Field image segmentation method afterwards. Eventually, the feasibility of the proposed image acquisition system and image processing methods is demonstrated through an experiment of titanium alloy machining. The proposed image acquisition system and image processing methods not only provide high quality detection of the integral spiral end milling cutter but can also be easily converted to detect other cutting systems with complex structures.

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