LoHi-WELD: A Novel Industrial Dataset for Weld Defect Detection and Classification, a Deep Learning Study, and Future Perspectives
Author:
Affiliation:
1. Department of Computing, Federal University of Technology-Paraná (UTFPR), Curitiba, Brazil
2. Department of Electrical Engineering, Federal University of Technology-Paraná (UTFPR), Curitiba, Brazil
Funder
National Council for Scientific and Technological Development [Conselho Nacional de Desenvolvimento Científico e Tecnológico]
Fundação Araucária
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/6287639/10380310/10540490.pdf?arnumber=10540490
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1. Preface: Review of Progress in Quantitative Nondestructive Evaluation
2. YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
3. Real-Time Quality Monitoring of Ultrathin Sheets Edge Welding Based on Microvision Sensing and SOCIFS-SVM
4. A novel penetration state recognition method based on LSTM with auditory attention during pulsed GTAW
5. Online defect recognition of narrow overlap weld based on two-stage recognition model combining continuous wavelet transform and convolutional neural network
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