Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Qiu, Yuanhong; Niu, Shuanlong; Niu, Tongzhi; Liu, Weifeng; Li, Bin

doi:10.3390/sym14071473

Cited by 4 publications

(6 citation statements)

References 31 publications

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“…Image processing approaches in surface defect inspection involve techniques like thresholding [4][5][6], image denoising and enhancement [7][8][9], to improve visibility and identify surface patterns for defect detection and classification. Improved Otsu's methods [4], [5] and global adaptive percentile thresholding [6] have been utilized for detecting steel surface defects.…”

Section: ) Image Processing-based Methodsmentioning

confidence: 99%

“…Morphological processing [7] and bilateral filtering [8] techniques have been employed to reduce noise and preserve edges. A joint-prior-based uneven illumination enhancement (JPUIE) method [9] has been used to eliminate uneven illumination in images for surface defect detection.…”

Section: ) Image Processing-based Methodsmentioning

confidence: 99%

“…In recent years, advances in image processing [4][5][6][7][8][9], machine learning [10][11][12][13][14][15], and deep learning [16][17][18][19][20] techniques have revolutionized surface inspection, enabling more accurate, efficient, and automated inspection processes. Image processing and machine learning approaches offer valuable tools for surface inspection, but they have limitations regarding feature selection and generalization.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Metal Surface Defect Recognition Through Image Patching and Synthetic Defect Generation

Mustafaev,

Kim,

Kim

2023

IEEE Access

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Preventing surface defects of metal products during the production process is challenging due to manufacturing complexity, material properties and environmental factors. Relying on human inspectors for quality control can introduce human error, which increases the risk of delivering defective products to customers. To address these challenges, we propose an Inception-CNN model specifically designed for surface defect recognition in servo motor housings (SMHs). The model incorporates an inception module between convolutional layers to effectively capture multi-scale local information and extract complex and abstract features. Additionally, we introduce an image patching technique that enhances defect recognition for small defects by reducing image complexity while maintaining defect visibility. Moreover, we propose a surface defect generation GAN (SDG-GAN) method, a novel approach that addresses the data imbalance problem and improves the accuracy and robustness of the classification model through generating diverse and high-quality synthetic defect images. The original data was collected using a line scan camera installed in the SMHSI system. We ensured model generalization through 10-fold cross-validation using the SMHSD-P-GAN dataset. Evaluation results indicate that our classification model outperformed other CNN models and achieved strong generalization, with 99.40% accuracy in cross-validation and 99.23% on the original test data. This represents a substantial 32.31% improvement over a baseline CNN model trained on the SMHSD-O-TA dataset, underscoring the effectiveness of our proposed approaches in enhancing classification performance Our method efficiently processes 12 images per second, making it ideal for real-time defect inspection in SMHs. Its successful integration into the SMHSI system confirms its practicality and effectiveness in real-world industrial applications.

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Section: ) Image Processing-based Methodsmentioning

confidence: 99%

Section: ) Image Processing-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancing Metal Surface Defect Recognition Through Image Patching and Synthetic Defect Generation

Mustafaev,

Kim,

Kim

2023

IEEE Access

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“…Moreover, some suspicious patterns can be marked as anomalous in a portion of the occurrences while as normal in the remaining ones, a problem commonly referred to as label noise. Such inconsistent annotations introduce a confounding pattern in the learning process due to biases in the ground truth [56]. Having a clean dataset is very complicated and recent works show that DL training is prone to overfit on corrupted labels since these latters excite more convolutional layers for the same class, thus resulting in a memorization effect [57], [58].…”

Section: Annotation Effort Requirements and Noisy Labelsmentioning

confidence: 99%

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

et al. 2023

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Automatic vision-based inspection systems have played a key role in product quality assessment for decades through the segmentation, detection, and classification of defects. Historically, machine learning frameworks, based on hand-crafted feature extraction, selection, and validation, counted on a combined approach of parameterized image processing algorithms and explicated human knowledge. The outstanding performance of deep learning (DL) for vision systems, in automatically discovering a feature representation suitable for the corresponding task, has exponentially increased the number of scientific articles and commercial products aiming at industrial quality assessment. In such a context, this article reviews more than 220 relevant articles from the related literature published until February 2023, covering the recent consolidation and advances in the field of fully-automatic DL-based surface defects inspection systems, deployed in various industrial applications. The analyzed papers have been classified according to a bi-dimensional taxonomy, that considers both the specific defect recognition task and the employed learning paradigm. The dependency on large and high-quality labeled datasets and the different neural architectures employed to achieve an overall perception of both well-visible and subtle defects, through the supervision of fine or/and coarse data annotations have been assessed. The results of our analysis highlight a growing research interest in defect representation power enrichment, especially by transferring pre-trained layers to an optimized network and by explaining the network decisions to suggest trustworthy retention or rejection of the products being evaluated.

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“…The authors in [21] propose a joint-prior-based uneven illumination enhancement method that improves defect detection using a particular semi-coupled retinex model.…”

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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Joint-Prior-Based Uneven Illumination Image Enhancement for Surface Defect Detection

Cited by 4 publications

References 31 publications

Enhancing Metal Surface Defect Recognition Through Image Patching and Synthetic Defect Generation

Enhancing Metal Surface Defect Recognition Through Image Patching and Synthetic Defect Generation

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

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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