Improvements for the Recognition Rate of Surface Defects of Aluminum Sheets

Liu, Xiaoming; Dongdong, Zhou

doi:10.1007/978-3-030-05864-7_66

Cited by 4 publications

(4 citation statements)

References 4 publications

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“…Light rays were emitted after passing through the hemispherical cover, which was also used as an intensity attenuator. The diffusion materials had a relatively flat reflectivity spectrum in the visible-light and near-infrared ranges, and their reflectance was 90% or higher [15]. The present study adopted circular light sources as the uniform light sources.…”

Section: Light Sources and Illumination Methodsmentioning

confidence: 99%

“…To increase the defect recognition rate for aluminum sheet surfaces, Liu et al [15] proposed the Nonsubsampled Shearlet Transform-Kernel Spectral Regression (NSST-KSR) method, a feature extraction method for flexibly extracting the multidimensional and multidirectional feature information of images. The aforementioned method can rapidly remove redundant signal noise and select crucial information as features.…”

Section: Introductionmentioning

confidence: 99%

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Light Source Modules for Defect Detection on Highly Reflective Metallic Surfaces

Huang¹,

Hsu²,

Liu³

et al. 2023

Metals

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High strength and highly reflective metal sheets are widely applied in industry; industrial requirements for defect detection are extremely demanding, particularly in the aviation and automotive industries. Classifying and recognizing surface defects on steel plate surfaces is highly challenging. Currently, defect detection is still inspected visually by personnel. However, given the high temperatures at inspection sites and the high risks in the operating environments, machine vision inspection systems are expected to replace manual inspection processes eventually. Therefore, this study developed an automated defect detection system that reduces the high reflectivity of examined objects. The light sources emitted light rays to the rays diffused and reflected multiple times inside the hemispherical cover to produce uniform illumination. Subsequently, image processing was conducted to highlight defect features on the stainless-steel plates. Relatively favorable light source positions were identified, which reduced the difficulty of class identification, the omission rate in defect detection to be decreased, and frequently encountered reflection problems in the automated optical inspection of metallic products to be overcome.

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Section: Light Sources and Illumination Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Light Source Modules for Defect Detection on Highly Reflective Metallic Surfaces

Huang¹,

Hsu²,

Liu³

et al. 2023

Metals

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“…An adaptive multiscale geometric analysis method named RNAMlet was proposed to identify surface defects of steel [8], which adapts its own computational cost according to the complexity of the image. Liu et al [9] constructed a new feature extraction method called NSST-KSR, which can effectively improve the recognition of surface defects in aluminum sheets. It combines both nonsubsampled shearlet transform (NSST) and kernel spectral regression (KSR) to extract features more efficiently, and then uses SVM for classification.…”

Section: Introductionmentioning

confidence: 99%

PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5

Chen,

Liu,

Wei

et al. 2024

PLoS ONE

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Solenoid connectors play important role in electronic stability system design, with the features of small size, low cost, fast response time and high reliability. The main production process challenge for solenoid connectors is the accurate detection of defects, which is closely related to safe driving. Both faultless and defective products have similar color and shape at the defect location, making proper inspection challenging. To address these issues, we proposed a defect detection model called PO-YOLOv5 to achieve accurate defect detection for solenoid connectors. First, an additional prediction head was added to enable the model to acquire more semantic information to detect larger-scale defective features. Second, we introduced dynamic convolution to learn complementary connections between the four dimensions of the convolution kernel by utilizing its multidimensional attention mechanism. Replacing conventional convolution with dynamic convolution enhances the detection accuracy of the model and reduces the inference time. Finally, we validated PO-YOLOv5 versus the state-of-the-art object detection methods on the same solenoid connectors dataset. Experiments revealed that our proposed approach exhibited higher accuracy. The mAP (mean Average Precision) result of PO-YOLOv5 was found to be about 90.1%. Compared with the original YOLOv5, PO-YOLOv5 exhibited improved precision by about 3%.

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“…In addition, defects may have different shapes and be of different types. This causes errors in their classification and recognition since certain defects are similar in shape and structure [3][4][5].…”

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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Improvements for the Recognition Rate of Surface Defects of Aluminum Sheets

Cited by 4 publications

References 4 publications

Light Source Modules for Defect Detection on Highly Reflective Metallic Surfaces

Light Source Modules for Defect Detection on Highly Reflective Metallic Surfaces

PO-YOLOv5: A defect detection model for solenoid connector based on YOLOv5

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

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