Recognition of Defective Mineral Wool Using Pruned ResNet Models

Review Computer Science, Information Systems

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

Mehdi Rafiei et al.

Summary: X-ray imaging technology has been widely used in clinical tasks for decades and has recently gained popularity in other fields such as industry, security, and geography. The development of computer vision and machine learning techniques has facilitated automatic processing of X-ray images, leading to the adoption of machine learning-based methods for X-ray image analysis. This survey provides an overview of recent research on computer vision and machine learning applications in X-ray analysis, covering applications, techniques, evaluation metrics, datasets, and performance comparison. The survey also highlights limitations and offers recommendations for future research in computer vision-based X-ray analysis.

IEEE ACCESS (2023)

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Article Automation & Control Systems

Deep Metric Learning-Based for Multi-Target Few-Shot Pavement Distress Classification

Hongwen Dong et al.

Summary: In this article, a new few-shot pavement distress detection method based on metric learning is proposed. The method can effectively learn new categories from a few labeled samples and outperforms other methods in experiments.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Automation & Control Systems

A New Cycle-consistent Adversarial Networks With Attention Mechanism for Surface Defect Classification With Small Samples

Long Wen et al.

Summary: This article proposes a new surface defect detection method called AttenCGAN, which utilizes cycle-consistent adversarial networks with attention mechanism. By synthesizing defect samples and enhancing features using attention mechanism, this method outperforms other published methods in terms of accuracy.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2022)

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Article Automation & Control Systems

Nondestructive Defect Detection in Castings by Using Spatial Attention Bilinear Convolutional Neural Network

Zhenhui Tang et al.

Summary: This article introduces a new method for X-ray image-based defective detection in castings, utilizing a combination of spatial attention mechanism and bilinear pooling in deep CNNs. Experimental studies demonstrate the advantages of the new method over existing ones.

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2021)

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Article Engineering, Multidisciplinary

Casting defect detection in X-ray images using convolutional neural networks and attention-guided data augmentation

Lili Jiang et al.

Summary: This paper proposes a weakly-supervised Convolutional Neural Network model based on X-ray images to recognize casting defects. The model achieves high test accuracy and recall through attention maps and data augmentation methods, demonstrating accuracy and robustness. The efficiency of the proposed approach is verified by comparing with existing methods and ablation experiments.

MEASUREMENT (2021)

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Article Automation & Control Systems