Detection of steel defect using the image processing algorithms

Sharifzadeh, Mehdi; Amirfattahi, Rassoul; Sadri, Saeed; Alirezaee, Shahpour; Ahmadi, Majid

doi:10.21608/iceeng.2008.34372

Cited by 18 publications

(23 citation statements)

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“…Thresholding methods are usually used to separate the defective regions on flat steel surfaces, which have been widely applied in online AVI systems [19,20]. The traditional thresholding methods identify defects by comparing the value of image pixels to a fix number and turn the test image into a simple binary frame, which is sensitive to random noises and non-uniform illuminations.…”

Section: ) Thresholdingmentioning

confidence: 99%

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Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

342

111

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Automated computer-vision-based defect detection has received much attention with the increasing surface quality assurance demands for the industrial manufacturing of flat steels. This paper attempts to present a comprehensive survey on surface defect detection technologies by reviewing about 120 publications over the last two decades for three typical flat steel products of con-casting slabs, hot-and cold-rolled steel strips. According to the nature of algorithms as well as image features, the existing methodologies are categorized into four groups: Statistical, spectral, model-based and machine learning. These literatures are summarized in this review to enable easy referral to suitable methods for diverse application scenarios in steel mills. Realization recommendations and future research trends are also addressed at an abstract level.

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Section: ) Thresholdingmentioning

confidence: 99%

“…Its applications can be found in fingerprint identification [84,85] and vehicle license plate recognition [86]. Interestingly, Sharifzadeh et al [20] applied HT to detect defects of holes, scratches, coil breaks and rusts on cold-rolled steel strips. However, it is difficult to raise the correct detection rates to more than 90%.…”

Section: ) Hough Transformmentioning

confidence: 99%

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Luo

Fang

Liu

et al. 2020

IEEE Trans. Instrum. Meas.

342

111

View full text Add to dashboard Cite

show abstract

“…Numerous studies have also been done in the identification of cracks in concrete [20] [21] asphalt [22] and pavement [17]. The algorithm employed by sharifzadeh et al [23] on the identification of holes, breaks, and rust using image processing involved thresholding of binary images and entropy study. The success rate of their method was 90.3% meanwhile Ghanta et al [24] study was based on wavelet transformation, but the rust identification involved cross-correlation model and analysis of colour but their method was limited by the size of the rust and image size, and in the end, it was only 52% effective.…”

Section: Related Workmentioning

confidence: 99%

Robotic Model for Unmanned Crack and Corrosion Inspection

Oyekola¹,

Mohamed*²,

Pumwa³

2019

IJITEE

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After prolonged usage of materials, the formation of cracks and corrosion initiates due to stress, loading condition, the environment of operation, etc. and this affects the structural integrity of structures. Periodic inspection of structures is usually planned, especially in industries where the impact of failure could be devastating, such as oil and gas pipelines, storage tanks, vessels, and airplanes, etc. which are just a few amongst others. This inspection is often aimed at detecting cracks and corrosion of internal and external components using several forms of non-destructive testing mechanism usually performed by a specialist at a high rate. To reduce the cost of inspection as well as downtime due to inspections and maintenance, deployments of mobile robots with fault tracking and identification purpose are steadily increasing. This paper, therefore, details the implementation of an image processing technique using MATLAB to identify defects of structural elements.

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“…Varieties of surface defects in different steel products are reported to be very high [3]. For example, Verlag Stahleisen [4] have categorised surface defects of hot-rolled products in nine main classes and 29 subclasses.…”

Section: Complexities Of Steel Surface Inspection Automationmentioning

confidence: 99%

Review of vision-based steel surface inspection systems

Neogi

Mohanta

Dutta³

2014

J Image Video Proc

233

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Steel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers' requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.

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Detection of steel defect using the image processing algorithms

Cited by 18 publications

References 0 publications

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Automated Visual Defect Detection for Flat Steel Surface: A Survey

Robotic Model for Unmanned Crack and Corrosion Inspection

Review of vision-based steel surface inspection systems

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