A layer-wise multi-defect detection system for powder bed monitoring: Lighting strategy for imaging, adaptive segmentation and classification

Shi, Binbin; Chen, Zhehan

doi:10.1016/j.matdes.2021.110035

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…Neural networks can mitigate fluctuations in inspection results caused by the manual inspection of abundant data; meanwhile, extensive calculations, which are performed via mathematical or computational models that mimic the structure of biological neural networks, are performed in machine learning and cognitive science to approximate neurological functions. Neural networks are adaptive systems, and the most typically used type of deep learning method in image processing is CNNs [23][24][25][26][27][28]. CNNs are advantageous owing to their capability to automatically extract features from images.…”

Section: Defect Detection Using Deep Learning With Attention Mapmentioning

confidence: 99%

“…In addition, image histogram equalization and Canny edge detection are typically used to improve the detection accuracy of image recognition systems for determining the dimensions of an object [20][21][22]. Recently, detection consistency has been improved using deep learning methods, particularly convolutional neural networks (CNNs) [23][24][25][26][27][28]. Among CNNs, VGG16 is trained using one million images, which contain 1000 categories that encompass almost all objects in daily life [29].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Low-Cost Real-Time Automated Optical Inspection Using Deep Learning and Attention Map

Shih¹,

Kuo²,

Lee³

2023

Intelligent Automation &Amp; Soft Computing

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Section: Defect Detection Using Deep Learning With Attention Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Cost Real-Time Automated Optical Inspection Using Deep Learning and Attention Map

Shih¹,

Kuo²,

Lee³

2023

Intelligent Automation &Amp; Soft Computing

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“…Shi and Chen [33] propose the defect detection algorithm for layer-wise of powder bed. CNN were applied to implement the classification of the defects, and their performances were evaluated and compared.…”

Section: B Deep-learning Techniquesmentioning

confidence: 99%

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

et al. 2022

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The final stage of the production process in the industry is quality control. Quality control answers the question of is there a defect on the surface of the products. Frequently the quality control is performed manually. The disadvantages of manual quality control are high error rate (low accuracy), low product rate (low performance) and high expense rate (high cost). The solution is automatic quality control using machine vision systems. These systems classify the products and segment the defects on their surfaces by processing the images taken by cameras during the production process in real-time. Some products like military cartridge cases have metallic, cylindrical, non-uniform texture and highly reflective surface. So, the quality of images is very important. Another factor that affects the accuracy is the nonuniform texture of the product surface. Distinguishing the product non-uniform texture from defect texture is a challenging problem. In previous works, this problem have been tried to be solved with image processing and deep learning techniques and the accuracy of 97% and 96% have been obtained, appropriately. According to NATO standards, the accuracy of the classification of the military cartridge cases should be above 99%. In this work, the methodology for classification of the military cartridge cases and segmentation of the defects on their surfaces with non-uniform texture is proposed to increase the accuracy. In scope of the proposed methodology the datasets with non-defective, defective and labeled/masked image classes of the cartridge cases were created, the deep learning models to classify the military cartridge cases and segment the defects on their surfaces were proposed, implemented and obtained results were evaluated using the metrics such as Accuracy, Precision, Recall, F1-Score, Jaccard Index (JI) and Mean Intersection over Union (mIoU). Obtained results showed that the proposed methodology increased the accuracy of classification to 100% with the DenseNet169 model and the F1-Score of segmentation to 92.1% with Improved U-Net and ResUnet models.

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“…An AI-based microstructural analysis typically considers an image as the input to a model and retrieves information from each part of the image to produce the segmented image. Image segmentation is a digital image processing technique which is widely used in the fields of engineering, medical image analysis, computer vision, etc 9 . for identifying the distinct regions or zones in the image that contain recognizable visual attributes 10 .…”

Section: Introductionmentioning

confidence: 99%

Microstructural segmentation using a union of attention guided U-Net models with different color transformed images

Biswas

Pramanik

Sen

et al. 2023

Sci Rep

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Metallographic images or often called the microstructures contain important information about metals, such as strength, toughness, ductility, corrosion resistance, which are used to choose the proper materials for various engineering applications. Thus by understanding the microstructures, one can determine the behaviour of a component made of a particular metal, and can predict the failure of that component in certain conditions. Image segmentation is a powerful technique for determination of morphological features of the microstructure like volume fraction, inclusion morphology, void, and crystal orientations. These are some key factors for determining the physical properties of metal. Therefore, automatic micro-structure characterization using image processing is useful for industrial applications which currently adopts deep learning-based segmentation models. In this paper, we propose a metallographic image segmentation method using an ensemble of modified U-Nets. Three U-Net models having the same architecture are separately fed with color transformed imaged (RGB, HSV and YUV). We improvise the U-Net with dilated convolutions and attention mechanisms to get finer grained features. Then we apply the sum-rule-based ensemble method on the outcomes of U-Net models to get the final prediction mask. We achieve the mean intersection over union (IoU) score of 0.677 on a publicly available standard dataset, namely MetalDAM. We also show that the proposed method obtains results comparable to state-of-the-art methods with fewer number of model parameters. The source code of the proposed work can be found at https://github.com/mb16biswas/attention-unet.

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A layer-wise multi-defect detection system for powder bed monitoring: Lighting strategy for imaging, adaptive segmentation and classification

Cited by 18 publications

References 25 publications

Low-Cost Real-Time Automated Optical Inspection Using Deep Learning and Attention Map

Low-Cost Real-Time Automated Optical Inspection Using Deep Learning and Attention Map

Deep Learning Based Classification of Military Cartridge Cases and Defect Segmentation

Microstructural segmentation using a union of attention guided U-Net models with different color transformed images

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