ArticleHeliyon2024
Deep convolutional neural network for weld defect classification in radiographic images.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- X-Ray Weld Image Detection Method of Water Injection Network Based on Sparse Representation.Sensors (Basel, Switzerland) · 2026Article
- A misclassification-aware explainable hybrid CNN-vision transformer framework for radiographic weld inspection.Scientific reports · 2026Article
- TIG welding defect detection using ResNet and Random Forest.PloS one · 2026Article
- On a Hybrid CNN-Driven Pipeline for 3D Defect Localisation in the Inspection of EV Battery Modules.Sensors (Basel, Switzerland) · 2025Article
- Utilization of a Deep Learning Algorithm for Automated Segmentation of Median Nerve from Ultrasound Obtained from the Distal Forearm and Wrist.Bioengineering (Basel, Switzerland) · 2025Article
- WeldVGG: A VGG-Inspired Deep Learning Model for Weld Defect Classification from Radiographic Images with Visual Interpretability.Sensors (Basel, Switzerland) · 2025Article
- Advanced Non-Destructive Testing Simulation and Modeling Approaches for Fiber-Reinforced Polymer Pipes: A Review.Materials (Basel, Switzerland) · 2025Review
- Automatic Detection and Classification of Natural Weld Defects Using Alternating Magneto-Optical Imaging and ResNet50.Sensors (Basel, Switzerland) · 2024Article
- Deep learning-based autonomous weld quality inspection in battery pack manufacturing using a two-stage model.Science progressArticle
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.