Online quality monitoring of perforated steel strips using an automated visual inspection (AVI) system

Younes, M. A.; Darwish, Samy H.; El‐Sayed, M. A.

doi:10.1109/icqr.2011.6031605

Cited by 5 publications

(6 citation statements)

References 13 publications

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“…Machine vision inspection is now a viable choice for various industrial applications [4][5][6] and detects dynamic objects for monocular recording [7]. Machine vision serves as a tool for quality control in automated production lines, finding applications in sectors like the food industry for examining surface defects [8][9], surface inspection [10], bottles [5,11], fruit [12][13], egg [14][15], nut [16][17], fabric [18], mechanical parts [19][20] and composite materials [21] as well as the pharmaceutical industry for inspecting medicine sheets. Notably, this method offers significant advantages, including continuous operation without the need for process interruptions or manual inspections [22].…”

Section: Introductionmentioning

confidence: 99%

Machine Vision for Detecting Defects in Liquid Bottles: An Industrial Application for Food and Packaging Sector

Omid Farhangi,

Ehsan Sheidaee,

Asma kisalaei

2024

Cloud Computing and Data Science

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The quality control of liquid packaging, such as cooking oils and beverages (bottled water, soft drinks, juices, etc.), is crucial due to the inherent risk of leakage. This process involves inspecting bottles for cap and seal ring defects and addressing issues arising from the gradual degradation of filling machines, leading to variations in the surface level of liquid bottles over time. Additionally, proper label placement significantly contributes to the customer-friendliness of a product. This research aims to introduce an automated vision-based rating system designed for the online inspection of defects in liquid bottles. The system is versatile, applicable to both academic and industrial settings, and can be easily adapted for use with various types of transparent liquid bottles. The defect detection metrics include three measures of distance determination and pattern matching. The equipment used in this study includes a Complementary Metal Oxide Semiconductor (CMOS) camera with a USB connection, a laptop, and a 14-speed conveyor belt, among other components. The system demonstrated an average accuracy of 95.6%, with specific accuracies for surface level, cap, and label placement at 100%, 95%, and 92%, respectively.

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Section: Introductionmentioning

confidence: 99%

Machine Vision for Detecting Defects in Liquid Bottles: An Industrial Application for Food and Packaging Sector

Omid Farhangi,

Ehsan Sheidaee,

Asma kisalaei

2024

Cloud Computing and Data Science

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“…Texture differences are perceived in object structure, roughness, orientation, smoothness and regularity, which are also commonly unique across processed workpieces [1]. The uses of texture analyses are widespread, such as for classification [2,3], retrieval [4], and inspection [5].…”

Section: Introductionmentioning

confidence: 99%

Texture-based Classification of Workpiece Surface Images using the Support Vector Machine

Ashour¹,

Halin²,

Khalid³

et al. 2015

IJSEIA

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“…Vision systems have been applied for many applications including inspection of surface defects [11,18], for Bottle inspection [2,11,12], for pharmaceutical tablets [15], for Press Part Sorting [16] and for Potatoes Defects Detection [17].…”

Section: Introductionmentioning

confidence: 99%

“…Using multiple image sensors at different levels of the production line and different angles of shots allows monitoring the different stages of the production process and collection of the information needed to assess product quality and flow of production (See figure 1) [1,2,3,4].…”

Section: Introductionmentioning

confidence: 99%

“…The detection of anomalies is transferred to the control system via discrete alarms on the industrial Gigabit Ethernet network. This system is mainly composed of a camera for taking pictures of products on the production line, special lighting to highlight faults, computer visualization and image processing related to alarm generation and automatic ejection [2].…”

Section: Introductionmentioning

confidence: 99%

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Design of Automatic Vision-based Inspection System for Monitoring in an Olive Oil Bottling Line

Abdelhedi¹,

Taouil²,

Hadjkacem³

2012

IJCA

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Automated vision inspection has become a vital part of the quality monitoring process. This paper compares the development and performance of two methodologies for a machine vision inspection system online for high speed conveyor. The first method developed is the Thresholding technique image processing algorithms and the second method is based on the edge detection. A case study was conducted to benchmark these two methods. Special effort has been put in the design of the defect detection algorithms to reach two main objectives: accurate feature extraction and on-line capabilities, both considering robustness and low processing time. An on-line implementation to inspect bottles is reported using new communication technique with GigE Vision camera and industrial Gigabit Ethernet network. The system is validated on olive oil bed. The implementation of our algorithm results in an effective real-time object tracking. The validity of the approach is illustrated by the presentation of experiment results obtained using the methods described in this paper.

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Online quality monitoring of perforated steel strips using an automated visual inspection (AVI) system

Cited by 5 publications

References 13 publications

Machine Vision for Detecting Defects in Liquid Bottles: An Industrial Application for Food and Packaging Sector

Machine Vision for Detecting Defects in Liquid Bottles: An Industrial Application for Food and Packaging Sector

Texture-based Classification of Workpiece Surface Images using the Support Vector Machine

Design of Automatic Vision-based Inspection System for Monitoring in an Olive Oil Bottling Line

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