Defectoscopic and Geometric Features of Defects That Occur in Sheet Metal and Their Description Based on Statistical Analysis

Konovalenko, І. V.; Maruschak, Pavlo; Kozbur, Halyna; Brezinová, Janette; Brezina, Jakub; Guzanová, Anna

doi:10.3390/met11111851

Cited by 15 publications

(3 citation statements)

References 27 publications

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“…First of all, the area was defined as the basic parameter that characterizes the damage size. To calculate other parameters, we used the "equivalent" ellipse approach (Figure 6) proposed by the authors of [10,[19][20][21][22]. The equivalent ellipse has a second moment equal to the moment of the defect recognized.…”

Section: Quantitative Parameters Of Damagementioning

confidence: 99%

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Influence of Uneven Lighting on Quantitative Indicators of Surface Defects

et al. 2022

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The impact of the illumination level on the quantitative indicators of mechanical damage of the rolled strip is investigated. To do so, a physical model experiment was conducted in the laboratory. The obtained images of defects at light levels in the range of 2–800 lx were recognized by a neural network model based on the U-net architecture with a decoder based on ResNet152. Two levels of illumination were identified, at which the total area of recognized defects increased: 50 lx and 300 lx. A quantitative assessment of the overall accuracy of defect recognition was conducted on the basis of comparison with data from images marked by an expert. The best recognition result (with Dice similarity coefficient ) was obtained for the illumination of 300 lx. At lower light levels (less than 200 lx), some of the damage remained unrecognized. At high light levels (higher than 500 lx), a decrease in DSC was observed, mainly due to the fact that the surface objects are better visible and the recognized fragments become wider. In addition, more false-positives fragments were recognized. The obtained results are valuable for further adjustment of industrial systems for diagnosing technological defects on rolled metal strips.

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Section: Quantitative Parameters Of Damagementioning

confidence: 99%

“…Therefore, wherever it is necessary to identify the main, most pronounced damage, low light may be appropriate. At the same time, the noise level in the image caused by an increased level of detail with respect to surface formations, which may not represent damage, is much lower [21,22].…”

Section: Investigating the Influence Of Lighting On The Recognition R...mentioning

confidence: 99%

Influence of Uneven Lighting on Quantitative Indicators of Surface Defects

et al. 2022

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“…Modern investigations showed that neural network-based defect detection methods allow high accuracy to be reached in the recognition of different classes of surface defects [22][23][24]. However, investigating and streamlining neural networks' capabilities and limitations in detecting, classifying, and calculating the parameters of the most common group defects appear crucial [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Surface Illumination as a Factor Influencing the Efficacy of Defect Recognition on a Rolled Metal Surface Using a Deep Neural Network

Maruschak,

Konovalenko,

Osadtsa

et al. 2024

Applied Sciences

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Modern neural networks have made great strides in recognising objects in images and are widely used in defect detection. However, the output of a neural network strongly depends on both the training dataset and the conditions under which the image was acquired for analysis. We have developed a software–hardware method for evaluating the effect of variable lighting on the results of defect recognition using a neural network model. The proposed approach allows us to analyse the recognition results of an existing neural network model and identify the optimal range of illumination at which the desired defects are recognised most consistently. For this purpose, we analysed the variability in quantitative parameters (area and orientation) of damage obtained at different degrees of illumination for two different light sources: LED and conventional incandescent lamps. We calculated each image’s average illuminance and quantitative parameters of recognised defects. Each set of parameters represents the results of defect recognition for a particular illuminance level of a given light source. The proposed approach allows the results obtained using different light sources and illumination levels to be compared and the optimal source type/illuminance level to be figured out. This makes implementing a defect detection environment that allows the best recognition accuracy and the most controlled product quality possible. An analysis of a steel sheet surface showed that the best recognition result was achieved at an illuminance of ~200 lx. An illuminance of less than ~150 lx does not allow most defects to be recognised, whereas an illuminance larger than ~250 lx increases the number of small objects that are falsely recognised as defects.

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A dual-structure attention-based multi-level feature fusion network for automatic surface defect detection

Zhang

Chen

et al. 2023

Vis Comput

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Defectoscopic and Geometric Features of Defects That Occur in Sheet Metal and Their Description Based on Statistical Analysis

Cited by 15 publications

References 27 publications

Influence of Uneven Lighting on Quantitative Indicators of Surface Defects

Influence of Uneven Lighting on Quantitative Indicators of Surface Defects

Surface Illumination as a Factor Influencing the Efficacy of Defect Recognition on a Rolled Metal Surface Using a Deep Neural Network

A dual-structure attention-based multi-level feature fusion network for automatic surface defect detection

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