Metal surface defect detection using iterative thresholding technique

Senthikumar, M.; Palanisamy, V.; Jain, Jaya

doi:10.1109/icctet.2014.6966360

Cited by 19 publications

(8 citation statements)

References 6 publications

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“…Defect classification uses many image features. They include linear decomposition methods [14,15], Gabor filters [16], fast Fourier transform (FFT) [17], wavelet transform [18], gray-level co-occurrence matrix (GLCM) [19] and local binary pattern (LBP) [20]. Many classification methods can be selected once the features are determined and they include a support vector machine (SVM) [21], decision tree [22] and random forest [23].…”

Section: Related Work 21 Machine Learning Methodsmentioning

confidence: 99%

Classification of Microscopic Laser Engraving Surface Defect Images Based on Transfer Learning Method

Zhang

Hao

et al. 2021

Electronics

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Microscopic laser engraving surface defect classification plays an important role in the industrial quality inspection field. The key challenges of accurate surface defect classification are the complete description of the defect and the correct distinction into categories in the feature space. Traditional classification methods focus on the terms of feature extraction and independent classification; therefore, feed handcrafted features may result in useful feature loss. In recent years, convolutional neural networks (CNNs) have achieved excellent results in image classification tasks with the development of deep learning. Deep convolutional networks integrate feature extraction and classification into self-learning, but require large datasets. The training datasets for microscopic laser engraving image classification are small; therefore, we used pre-trained CNN models and applied two fine-tuning strategies. Transfer learning proved to perform well even on small future datasets. The proposed method was evaluated on the datasets consisting of 1986 laser engraving images captured by a metallographic microscope and annotated by experienced staff. Because handcrafted features were not used, our method is more robust and achieves better results than traditional classification methods. Under five-fold-validation, the average accuracy of the best model based on DenseNet121 is 96.72%.

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Section: Related Work 21 Machine Learning Methodsmentioning

confidence: 99%

Classification of Microscopic Laser Engraving Surface Defect Images Based on Transfer Learning Method

Zhang

Hao

et al. 2021

Electronics

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“…• Image processing-based algorithms rely on various image filtering techniques and threshold values to recognize defects on metal surface. Senthikumar et al applied a spatial filter to get rid of noise and transform the input image to a binary image, where the white pixels were detected as defects [3]. In [4], a polarized light-filtering based method was developed to enhance the contrast of defect areas and suppress the noise of flawless areas.…”

Section: Metal Surface Defect Detectionmentioning

confidence: 99%

An Enhanced YOLOv4 Model With Self-Dependent Attentive Fusion and Component Randomized Mosaic Augmentation for Metal Surface Defect Detection

2022

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Metal surface quality control is significant in the production line of metal products. Detecting metal surface defects is challenging due to the various types and morphological patterns. Recent advances have witnessed deep learning-based automated optical inspection systems as a promising solution. This paper presents an enhanced YOLOv4 model for metal surface defect detection (MSDD). Specifically, we integrate three boosting components into the original YOLOv4, including 1) a self-dependent attentive fusion (SAF) block, placed within the model neck, to enhance inter-path and cross-layer feature fusion, 2) a component randomized Mosaic augmentation (CRMA) scheme to strategically discourage an overtransformed image to participate in training, and 3) a perturbation agnostic (PA) label smoothing method to keep the model from making over-confident predictions and thus act as a means of regularization. The proposed method has been validated on a self-developed MSDD dataset. It is shown that each boosting component can lead to an impressive mAP gain, and the final model outperforms the baselines,

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“…Therefore, detection precision is the main metric to evaluate the detection method. There have been three types of automated inspection techniques based on production images: 1) image processing [1] - [3]. The technique focuses on using image filters to highlight defect instances.…”

Section: Introductionmentioning

confidence: 99%

MeDERT: A Metal Surface Defect Detection Model

Wang

Xie

2023

IEEE Access

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Defects in various products are unavoidable because of measurement errors and equipment accuracy limitations in the production process. Recent advances in metal surface defect detection have focused on optimizing traditional methods, developing new detection techniques, and exploring deep learning-based algorithms, providing technological support to improve metal manufacturing quality and production efficiency. To ensure the highest yield rate and meet production requirements, all products must undergo defect inspections before leaving the factory. However, Traditional methods for detecting metal surface defects require a lot of manual involvement, are difficult to accurately detect small defects, are susceptible to environmental interference, and lack stability and reliability. To address this issue, we propose the MeDERT model for metal surface defect detection. Our approach involves a new Spansensitive Texture Fusion (STF) module structure that focuses on multi-headed attention modules to recover lost details and boost inspection speed and on top of that use the Jump-sensitive detail recovery feature fusion module to ensure the validity of the extracted textures. Additionally, we introduce singular value decomposition and pretzel noise to model the noise and enhance model robustness through data augmentation. Our MeDERT model achieved state-of-the-art (SOTA) results on a specified dataset, demonstrating its effectiveness in enhancing inspection efficiency and accuracy.

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Metal surface defect detection using iterative thresholding technique

Cited by 19 publications

References 6 publications

Classification of Microscopic Laser Engraving Surface Defect Images Based on Transfer Learning Method

Classification of Microscopic Laser Engraving Surface Defect Images Based on Transfer Learning Method

An Enhanced YOLOv4 Model With Self-Dependent Attentive Fusion and Component Randomized Mosaic Augmentation for Metal Surface Defect Detection

MeDERT: A Metal Surface Defect Detection Model

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