Palma-Ramírez Dayana, Ross-Veitía Bárbara D, Font-Ariosa Pablo, Espinel-Hernández Alejandro, Sanchez-Roca Angel, Carvajal-Fals Hipólito, Nuñez-Alvarez José R, Hernández-Herrera Hernan
Postgraduate Program Doctorate in Applied Computer Engineering School of Computer Engineering. University of Valparaiso. Valparaiso, Chile.
Production Engineering Doctorate Postgraduate Program Federal Technological University of Paraná (UTFPR) - Ponta Grossa Campus. PR, Brazil.
Heliyon. 2024 May 1;10(9):e30590. doi: 10.1016/j.heliyon.2024.e30590. eCollection 2024 May 15.
The quality of welds is critical to the safety of structures in construction, so early detection of irregularities is crucial. Advances in machine vision inspection technologies, such as deep learning models, have improved the detection of weld defects. This paper presents a new CNN model based on ResNet50 to classify four types of weld defects in radiographic images: crack, pore, non-penetration, and no defect. Stratified cross-validation, data augmentation, and regularization were used to improve generalization and avoid over-fitting. The model was tested on three datasets, RIAWELC, GDXray, and a private dataset of low image quality, obtaining an accuracy of 98.75 %, 90.255 %, and 75.83 %, respectively. The model proposed in this paper achieves high accuracies on different datasets and constitutes a valuable tool to improve the efficiency and effectiveness of quality control processes in the welding industry. Moreover, experimental tests show that the proposed approach performs well on even low-resolution images.
焊缝质量对建筑结构的安全至关重要，因此早期发现不规则情况至关重要。诸如深度学习模型等机器视觉检测技术的进步，提高了焊缝缺陷的检测能力。本文提出了一种基于ResNet50的新型卷积神经网络（CNN）模型，用于对射线图像中的四种焊缝缺陷进行分类：裂纹、气孔、未焊透和无缺陷。采用分层交叉验证、数据增强和正则化来提高泛化能力并避免过拟合。该模型在三个数据集RIAWELC、GDXray和一个低图像质量的私有数据集上进行了测试，分别获得了98.75%、90.255%和75.83%的准确率。本文提出的模型在不同数据集上均取得了较高的准确率，构成了提高焊接行业质量控制流程效率和有效性的宝贵工具。此外，实验测试表明，所提出的方法在低分辨率图像上也表现良好。
