scholarly journals Computer Vision Method in Beer Quality Evaluation—A Review

Beverages ◽  
2019 ◽  
Vol 5 (2) ◽  
pp. 38 ◽  
Author(s):  
Jasmina Lukinac ◽  
Kristina Mastanjević ◽  
Krešimir Mastanjević ◽  
Gjore Nakov ◽  
Marko Jukić
Keyword(s):  
Computer Vision ◽  
Product Quality ◽  
Quality Evaluation ◽  
Food Product ◽  
Quality Inspection ◽  
External Quality ◽  
Visual Appearance ◽  
Manual Inspection ◽  
Inspection Systems ◽  
Non Destructive

Beers are differentiated mainly according to their visual appearance and their fermentation process. The main quality characteristics of beer are appearance, aroma, flavor, and mouthfeel. Important visual attributes of beer are foam appearance (volume and persistence), as well as the color and clarity. To replace manual inspection, automatic, objective, rapid and repeatable external quality inspection systems, such as computer vision, are becoming very important and necessary. Computer vision is a non-contact optical technique, suitable for the non-destructive evaluation of the food product quality. Currently, the main application of computer vision occurs in automated inspection and measurement, allowing manufacturers to keep control of product quality. This paper presents an overview of the applications and the latest achievements of the computer vision methods in determining the external quality attributes of beer.

scholarly journals Product Inspection Methodology via Deep Learning: An Overview

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5039
Author(s):  
Tae-Hyun Kim ◽  
Hye-Rin Kim ◽  
Yeong-Jun Cho
Keyword(s):  
Deep Learning ◽  
Product Quality ◽  
Inspection System ◽  
Quality Inspection ◽  
Learning Models ◽  
Unified Framework ◽  
Learning Techniques ◽  
Inspection Systems ◽  
System Maintenance ◽  
Test Scenarios

In this study, we present a framework for product quality inspection based on deep learning techniques. First, we categorize several deep learning models that can be applied to product inspection systems. In addition, we explain the steps for building a deep-learning-based inspection system in detail. Second, we address connection schemes that efficiently link deep learning models to product inspection systems. Finally, we propose an effective method that can maintain and enhance a product inspection system according to improvement goals of the existing product inspection systems. The proposed system is observed to possess good system maintenance and stability owing to the proposed methods. All the proposed methods are integrated into a unified framework and we provide detailed explanations of each proposed method. In order to verify the effectiveness of the proposed system, we compare and analyze the performance of the methods in various test scenarios. We expect that our study will provide useful guidelines to readers who desire to implement deep-learning-based systems for product inspection.

Non-destructive and contactless quality evaluation of table grapes by a computer vision system

2019 ◽  
Vol 156 ◽  
pp. 558-564 ◽  
Author(s):  
Dario Pietro Cavallo ◽  
Maria Cefola ◽  
Bernardo Pace ◽  
Antonio Francesco Logrieco ◽  
Giovanni Attolico
Keyword(s):  
Computer Vision ◽  
Quality Evaluation ◽  
Vision System ◽  
Computer Vision System ◽  
Table Grapes ◽  
Non Destructive

Automatic Product Quality Inspection Using Computer Vision Systems

2014 ◽  
pp. 135-156 ◽  
Author(s):  
Osslan Osiris Vergara-Villegas ◽  
Vianey Guadalupe Cruz-Sánchez ◽  
Humberto de Jesús Ochoa-Domínguez ◽  
Manuel de Jesús Nandayapa-Alfaro ◽  
Ángel Flores-Abad
Keyword(s):  
Computer Vision ◽  
Product Quality ◽  
Quality Inspection ◽  
Vision Systems ◽  
Computer Vision Systems ◽  
Automatic Product

Automated Computer Vision System Based on Color Concentration and Level for Product Quality Inspection

2018 ◽  
pp. 297-310
Author(s):  
Nor Nabilah Syazana Abdul Rahman ◽  
Norhashimah Mohd Saad ◽  
Abdul Rahim Abdullah ◽  
Farhan Abdul Wahab
Keyword(s):  
Computer Vision ◽  
Product Quality ◽  
Vision System ◽  
Quality Inspection ◽  
Computer Vision System

The Development for Green Channel Cargo Inspection System

2012 ◽  
Vol 152-154 ◽  
pp. 1736-1740
Author(s):  
Yuan Ping Shi ◽  
Jing Sheng Yu ◽  
Li Qin Zhang ◽  
Hong Qiang Sun ◽  
Pu Hao
Keyword(s):  
Low Cost ◽  
Inspection System ◽  
Non Destructive Testing ◽  
Standard Samples ◽  
Destructive Testing ◽  
Green Channel ◽  
Manual Inspection ◽  
Cargo Inspection ◽  
Inspection Systems ◽  
Non Destructive

We designed a set of non-destructive system which is based on ray inspection systems, it mainly used to detect the Green Channel Cargo, and the result will be given out within 30 seconds. The ray coefficient of the Green Channel Cargo are different from the other transport cargo, so we can measure the absorption coefficient and compare it with the standard samples which is pre-built. The moved radioactive source and detector are designed, in order to prevent the mixed goods, we designed the removable radioactive sources and detectors, so the goods can be scanned up and down and around. Once the goods are mixed together, the ray coefficient which is calculated synchronously will be abnormal, parking and manual inspection is a supplementary means, so as to achieve a low-cost non-destructive testing.

Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review

2014 ◽  
Vol 62 ◽  
pp. 326-343 ◽  
Author(s):  
Baohua Zhang ◽  
Wenqian Huang ◽  
Jiangbo Li ◽  
Chunjiang Zhao ◽  
Shuxiang Fan ◽  
...  
Keyword(s):  
Computer Vision ◽  
Fruits And Vegetables ◽  
Quality Inspection ◽  
External Quality
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