Adaptive depth and receptive field selection network for defect semantic segmentation on castings X-rays

Han, Yonghao; Li, Xingjie; Song, Kun; Shang, Erfeng; Liu, Huiyu; Yan, Yunhui

doi:10.1016/j.ndteint.2020.102345

Cited by 30 publications

(12 citation statements)

References 21 publications

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“…Semantic segmentation methods for casting inspection were assessed in [45], [46]. Authors in [45] used only realistically simulated X-ray data to train a network to perform semantic segmentation on cast aluminum parts.…”

Section: B Castingmentioning

confidence: 99%

“…Authors in [45] used only realistically simulated X-ray data to train a network to perform semantic segmentation on cast aluminum parts. Large defect scale variation, small inter-class differences, and annotation uncertainty issues were tackled in [46] for defect semantic segmentation.…”

Section: B Castingmentioning

confidence: 99%

“…These factors make it easier to pass information through the networks, which allowed to have a larger number of layers and a smaller error rate on both train and test sets than the earlier CNN models. Due to its benefits, different variants of ResNet with various depths, such as ResNet-18 [85], ResNet-34 [95], ResNet-50 [66], and ResNet-101 [44], have been used in X-ray data assessment studies, e.g., in casting defect recognition [44], [46] and detection of internal defects in the Aluminum Conductor Composite Core (ACCC) [90].…”

Section: ) Deep Classification and Backbone Architecturesmentioning

confidence: 99%

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Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

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X-ray imaging technology has been used for decades in clinical tasks to reveal the internal condition of different organs, and in recent years, it has become more common in other areas such as industry, security, and geography. The recent development of computer vision and machine learning techniques has also made it easier to automatically process X-ray images and several machine learningbased object (anomaly) detection, classification, and segmentation methods have been recently employed in X-ray image analysis. Due to the high potential of deep learning in related image processing applications, it has been used in most of the studies. This survey reviews the recent research on using computer vision and machine learning for X-ray analysis in industrial production and security applications and covers the applications, techniques, evaluation metrics, datasets, and performance comparison of those techniques on publicly available datasets. We also highlight some drawbacks in the published research and give recommendations for future research in computer vision-based X-ray analysis.

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Section: B Castingmentioning

confidence: 99%

Section: B Castingmentioning

confidence: 99%

Section: ) Deep Classification and Backbone Architecturesmentioning

confidence: 99%

See 1 more Smart Citation

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Rafiei

Raitoharju²,

Iosifidis

2023

IEEE Access

View full text Add to dashboard Cite

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“…Furthermore, according to the specific visual inspection application scenario, additional defect segmentation methods have been continuously proposed. For example, Yu et al [260] proposed an adaptive depth and receptive field selection network. In this method, an adaptive depth selection mechanism was designed to extract features of various depths, and an adaptive receptive field block was proposed to select the best acceptance domain.…”

Section: Characteristicsmentioning

confidence: 99%

State of the Art in Defect Detection Based on Machine Vision

Ren

Fang

Yan

et al. 2021

Int. J. of Precis. Eng. and Manuf.-Green Tech.

350

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Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

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“…After 2012, thanks to the increasing of the amount of data, the improvement of computer computing power, and the emergence of new machine learning algorithm (deep learning, DL), the AI application began to explode. The AI technology has been applied in different fields, [3][4][5][6] including the field of industrial inspection. [7][8][9] Many scholars have put forward the "deep learning þ defect classification" method, which has been applied to the classification of surface defects of steel strip.…”

Section: Introductionmentioning

confidence: 99%

Surface Defect Classification of Steel Strip with Few Samples Based on Dual‐Stream Neural Network

Zhang

Yan

et al. 2021

steel research int.

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The automatic classification of surface defects is significant to the steel strip inspection system. However, influenced by the lack of samples, it is difficult to improve the classification accuracy. Herein, a novel dual‐stream neural network is proposed, which is composed by two streams of sample generation and classification training. Subsequently, numerous defect samples are generated for the classifier pretraining, and the real steel strip surface defects are classified by the transfer learning method. The experiment results demonstrate that the dual‐stream neural network has the highest classification accuracy of 99.70% among the state‐of‐the‐art classifiers, and the classification accuracy increasesd by 9.67% through generating plentiful similar images for pretraining. Moreover, the convolutional neural network (CNN) can improve the quality of generated defect images. When the number of epochs reaches 2000, the generated images are closest to the real labeled images. Finally, the cross‐validation proves that the dual‐stream neural network has excellent stability and robustness, without over‐fitting problem in the training process.

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Adaptive depth and receptive field selection network for defect semantic segmentation on castings X-rays

Cited by 30 publications

References 21 publications

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

Computer Vision on X-Ray Data in Industrial Production and Security Applications: A Comprehensive Survey

State of the Art in Defect Detection Based on Machine Vision

Surface Defect Classification of Steel Strip with Few Samples Based on Dual‐Stream Neural Network

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