scholarly journals INDUSTRIAL VISION SYSTEM FOR OBJECT DETECTION

2021 ◽  
pp. 99-105
Author(s):  
Marek Vagas ◽  
Alena Galajdova
Keyword(s):  
Object Detection ◽  
Visual Inspection ◽  
Vision System ◽  
Robotic Arm ◽  
Reference Object ◽  
Inspection System ◽  
Technological Operation ◽  
Lighting Conditions ◽  
The Future ◽  
Automated Process

The article aimed to experimentally verify and detect the coordinates of a given reference object, which will be manipulated by an industrial robotic arm, type SCARA. It was necessary to identify and locate individual objects at the automated workplace using the OMRON F150-3 visual inspection system during the process. Therefore, the ultimate goal of the assigned task is to reliably grasp the detected reference object and move on to the next technological operation. In the future, it would be appropriate to ensure reliable lighting conditions to guarantee the continuity of the automated process. The article is a publication of scientific and methodical character.

Improved Surface Roughness Evaluation of Ground Components Using Illumination Compensated Image: A Machine Vision Approach

2016 ◽  
Author(s):  
Jibin G. John ◽  
Arunachalam Narayanaperumal
Keyword(s):  
Surface Roughness ◽  
Machine Vision ◽  
System Development ◽  
Vision System ◽  
Inspection System ◽  
Work Piece ◽  
Lighting Conditions ◽  
Image Characteristics ◽  
Texture Parameters ◽  
Roughness Evaluation

One of the crucial elements of image based inspection system development is the lighting conditions. It directly defines the quality of the image which in turn affects the accuracy and robustness of an inspection procedure using machine vision system. The common image characteristics change with variation in lighting leading to large image differences. In recent years, evaluation of surface roughness of a work piece by machine vision has received a great deal of attention. However, practical surface roughness instruments based on machine vision are still difficult to develop for application specific online assessment in particular. This is due to the fact that the images taken from the machined surfaces are affected by illumination, reflectivity and ambience during the image acquisition process. This lighting inhomogeneity is considered to be a disturbing signal component, which should be suppressed to achieve consistency in surface roughness quantification. In this paper, the illumination compensated images are used for surface roughness evaluation. The homomorphic filtering and Discrete Cosine Transform (DCT) based normalization techniques are utilized to remove the illumination inhomogeneity and the performance of these techniques were compared. The results clearly indicate that it is important to consider the lighting conditions when the machine vision approach is used to quantify the surface texture parameters.

scholarly journals Inspección mecánico-visual de ampolletas farmacéuticas de vidrio mediante el uso de visión artificial y robótica

2019 ◽  
pp. 1-4
Author(s):  
Felipe De Jesús Garcia-Gutierez ◽  
Israel Alejandro Rojas-Olmedo ◽  
Alma Baron- Guadarrama ◽  
Luis Fernando Sanchez-Mancilla
Keyword(s):  
Pharmaceutical Industry ◽  
Direct Current ◽  
Visual Inspection ◽  
Conveyor Belt ◽  
Dc Motor ◽  
Artificial Vision ◽  
Inspection System ◽  
Automatic Process ◽  
Vacuum Suction ◽  
Automated Process

One of the problems currently faced by the pharmaceutical industry in an automatic process, is to ensure that by means of a single traditional inspection system, the characteristics of the glass ampules are evaluated with respect to their appearance in the finish and the color. In addition to being free of defects such as cracks, roughness, excess material and burrs in the finished product. In the present work, a prototype of automatic artificial vision was developed, reliable and safe for the mechanical-visual inspection, ordering and packaging of ampoules in the pharmaceutical industry. The elements that make up the system are: a robot of the brand Mitsubishi RV-2FB-D, the controller CR-750D, a camera COGNEX model ISM 1100-C11, a conveyor belt, a direct current (DC) motor and a vacuum suction cups, the latter responsible for taking the ampule. The results obtained in the project allowed to validate the use of artificial vision to visually and visually verify amber-colored ampoules in an automated process, as well as to validate the ordering of ampoules in the same position inside their packaging.

scholarly journals Enhanced Manufacturing Execution System “MES” Through a Smart Vision System

2021 ◽  
pp. 329-334
Author(s):  
Chawki El Zant ◽  
Quentin Charrier ◽  
Khaled Benfriha ◽  
Patrick Le Men
Keyword(s):  
Control Systems ◽  
Real Time ◽  
Visual Inspection ◽  
Vision System ◽  
Processing System ◽  
Inspection System ◽  
Manufacturing Execution System ◽  
Novel Approach ◽  
Industrial Performance ◽  
Security Operations

AbstractThe level of industrial performance is a vital issue for any company wishing to develop and acquire more market share. This article presents a novel approach to integrate intelligent visual inspection into “MES” control systems in order to gain performance. The idea is to adapt an intelligent image processing system via in-situ cameras to monitor the production system. The images are thus analyzed in real time via machine learning interpreting the visualized scene and interacting with some features of the MES system, such as maintenance, quality control, security, operations, etc. This novel technological brick, combined with the flexibility of production, contributes to optimizing the system in terms of autonomy and responsiveness to detect anomalies, already encountered, or even new ones. This smart visual inspection system is considered as a Cyber Physical System CPS brick integrated to the manufacturing system which will be considered an edge computing node in the final architecture of the platform. This smart CPS represents the 1st level of calculation and analysis in real time due to embedded intelligence. Cloud computing will be a perspective for us, which will represent the 2nd level of computation, in deferred time, in order to analyze the new anomalies encountered and identify potential solutions to integrate into MES. Ultimately, this approach strengthens the robustness of the control systems and increases the overall performance of industrial production.

Improvement of Corrosion Detection Using Vision System for Pipeline Inspection

2015 ◽  
Vol 761 ◽  
pp. 125-131 ◽  
Author(s):  
Syahril Anuar Idris ◽  
Fairul Azni Jafar ◽  
Zamberi Jamaludin ◽  
Noraidah Blar
Keyword(s):  
Preliminary Data ◽  
Visual Inspection ◽  
Vision System ◽  
New Method ◽  
Inspection System ◽  
Visual Data ◽  
Corrosion Detection ◽  
Pipeline Inspection

Nowadays, the utilization of cameras as an inspection tool has been increasing. The flexibility functions of camera fits to get different kind of information. This research is focusing on developing a robust visual inspection system for corrosion detection that is able to detect corrosion in any environment, and the corrosion detection will be using visual data as primary tools. A review on current pipeline inspection would give a brief detail on the improvement of the proposed inspection system. Furthermore, the inadequacies of the proposed visual corrosion detection are identified and discussed from the reviewing process on existing researches and analysis on preliminary data obtained. It is expected that the output of the proposed system will be a new method of corrosion detection and pioneer for the inspection system on robust environment.

scholarly journals R-CNN-Based Large-Scale Object-Defect Inspection System for Laser Cutting in the Automotive Industry

Processes ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 2043
Author(s):  
Donggyun Im ◽  
Jongpil Jeong
Keyword(s):  
Object Detection ◽  
Laser Cutting ◽  
Large Scale ◽  
Metal Cutting ◽  
Vision System ◽  
Model Development ◽  
Inspection System ◽  
Defect Inspection ◽  
Molding Process ◽  
Inspection Method

A car side-outer is an iron mold that is applied in the design and safety of the side of a vehicle, and is subjected to a complicated and detailed molding process. The side-outer has three features that make its quality inspection difficult to automate: (1) it is large; (2) there are many objects to inspect; and (3) it must fulfil high-quality requirements. Given these characteristics, the industrial vision system for the side-outer is nearly impossible to apply, and indeed there is no reference for an automated defect-inspection system for the side-outer. Manual inspection of the side-outer worsens the quality and cost competitiveness of the metal-cutting companies. To address these problems, we propose a large-scale Object-Defect Inspection System based on Regional Convolutional Neural Network (R-CNN; RODIS) using Artificial Intelligence (AI) technology. In this paper, we introduce the framework, including the hardware composition and the inspection method of RODIS. We mainly focus on creating the proper dataset on-site, which should be prepared for data analysis and model development. Additionally, we share the trial-and-error experiences gained from the actual installation of RODIS on-site. We explored and compared various R-CNN backbone networks for object detection using actual data provided by a laser-cutting company. The Mask R-CNN models using Res-net-50-FPN show Average Precision (AP) of 71.63 (Object Detection) and 86.21 (Object Seg-mentation), which indicates a better performance than that of other models.

scholarly journals Automated Detection of Tetranychus urticae Koch in Citrus Leaves Based on Colour and VIS/NIR Hyperspectral Imaging

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 1002
Author(s):  
María Gyomar Gonzalez-Gonzalez ◽  
Jose Blasco ◽  
Sergio Cubero ◽  
Patricia Chueca
Keyword(s):  
Hyperspectral Imaging ◽  
Tetranychus Urticae ◽  
Visual Inspection ◽  
Vision System ◽  
Automated Detection ◽  
Nutritional Deficiencies ◽  
Phyllocnistis Citrella ◽  
Mature Leaves ◽  
Citrus Leaves ◽  
Tetranychus Urticae Koch

Tetranychus urticae Koch is an important citrus pest that produces chlorotic spots on the leaves and scars on the fruit of affected trees. It is detected by visual inspection of the leaves. This work studies the potential of colour and hyperspectral imaging (400–1000 nm) under laboratory conditions as a fast and automatic method to detect the damage caused by this pest. The ability of a traditional vision system to differentiate this pest from others, such as Phyllocnistis citrella, and other leaf problems such as those caused by nutritional deficiencies, has been studied and compared with a more advanced hyperspectral system. To analyse the colour images, discriminant analysis has been used to classify the pixels as belonging to either a damaged or healthy leaves. In contrast, the hyperspectral images have been analysed using PLS DA. The rate of detection of the damage caused by T. urticae with colour images reached 92.5%, while leaves that did not present any damage were all correctly identified. Other problems such as damage by P. citrella were also correctly discriminated from T. urticae. Moreover, hyperspectral imaging allowed damage caused by T. urticae to be discriminated from healthy leaves and to distinguish between recent and mature leaves, which indicates whether it is a recent or an older infestation. Furthermore, good results were achieved in the discrimination between damage caused by T. urticae, P. citrella, and nutritional deficiencies.

scholarly journals A novel visual inspection system for rail surface spalling detection

2021 ◽  
Vol 1048 (1) ◽  
pp. 012015
Author(s):  
Dieuthuy Pham ◽  
Minhtuan Ha ◽  
Changyan Xiao
Keyword(s):  
Visual Inspection ◽  
Inspection System

Visual inspection system using multidirectional 3-D imager

1991 ◽  
Author(s):  
Tetsuo Koezuka ◽  
Yoshikazu Kakinoki ◽  
Shinji Hashinami ◽  
Masato Nakashima
Keyword(s):  
Visual Inspection ◽  
Inspection System
Sign in / Sign up

Export Citation Format

Share Document