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Journals >Chinese Journal of Lasers >Volume 52 >Issue 12 >Page 1202105 > Article

Chinese Journal of Lasers
Vol. 52, Issue 12, 1202105 (2025)

A Lightweight Real‐Time Weld Defect Classification Model

Yunhao Li^*, Chengtie Li, and Qiuming Li^**

Author Affiliations

School of Control Engineering, Northeastern University at Qinhuangdao Campus, Qinhuangdao 066004, Hebei , China

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DOI: 10.3788/CJL241390 Cite this Article Set citation alerts

Yunhao Li, Chengtie Li, Qiuming Li. A Lightweight Real‐Time Weld Defect Classification Model[J]. Chinese Journal of Lasers, 2025, 52(12): 1202105 Copy Citation Text

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References

[1] Vasan V, Sridharan N V, Balasundaram R J et al. Ensemble-based deep learning model for welding defect detection and classification[J]. Engineering Applications of Artificial Intelligence, 136, 108961(2024).

[2] Li C. Research on weld surface defect detection algorithm based on deep learning[D], 1-2(2024).

[3] Zhang B X, Wang X P, Cui J H et al. Welding defects classification by weakly supervised semantic segmentation[J]. NDT & E International, 138, 102899(2023).

[4] Pydi H P, Pradeep A, Vijayakumar S et al. Examination of various weld process parameters in MIG welding of carbon steel on weld quality using radiography & magnetic particle testing[J]. Materials Today: Proceedings, 62, 1909-1912(2022).

[5] Zhan C, Peng X Y, Li Y. Nondestructive testing cracks on inner surface of thick pipes by laser ultrasonic visualization[J]. Laser & Optoelectronics Progress, 58, 0628004(2021).

[6] Cheng S, Yang H G, Xu X Q et al. Improved lightweight X-ray aluminum alloy weld defects detection algorithm based on YOLOv5[J]. Chinese Journal of Lasers, 49, 2104005(2022).

[7] Liu Y, Yuan K, Li T et al. NDT method for line laser welding based on deep learning and one-dimensional time-series data[J]. Applied Sciences, 12, 7837(2022).

[8] Huang W, Kovacevic R. Development of a real-time laser-based machine vision system to monitor and control welding processes[J]. The International Journal of Advanced Manufacturing Technology, 63, 235-248(2012).

[9] Nguyen H C, Lee B R. Laser-vision-based quality inspection system for small-bead laser welding[J]. International Journal of Precision Engineering and Manufacturing, 15, 415-423(2014).

[10] Li J Y, Li C X, Wang X L. Research on feature extraction of weld defect image[J]. Welding Technology, 47, 78-82, 6(2018).

[11] Cai W, Jiang P, Shu L S et al. Machine vision-based spatter monitoring method and spatter characterization for high power laser welding process[J]. Chinese Journal of Lasers, 50, 2402106(2023).

[12] Huang Y M, Wu D, Zhang Z F et al. EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM[J]. Journal of Materials Processing Technology, 239, 92-102(2017).

[13] Wu L, Chu Y K, Yang H G et al. Aluminum alloy weld DR image defect detection technology based on YOLOv7TS[J]. Chinese Journal of Lasers, 51, 2002102(2024).

[14] Yue J F, Li W M, Ning L H et al. Research on real-time weld defect detection algorithm based on YOLO-DEFW[J]. Chinese Journal of Lasers, 52, 0802403(2025).

[15] Chen L L, Li G, Zhang S K et al. YOLO-SAG: an improved wildlife object detection algorithm based on YOLOv8n[J]. Ecological Informatics, 83, 102791(2024).

[16] Ding X H, Zhang X Y, Ma N N et al. RepVGG: making VGG-style ConvNets great again[C], 13728-13737(2021).

[17] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Computer vision‒ECCV 2018, 11211, 3-19(2018).

[18] Zhang H, Zu K K, Lu J et al. EPSANet: an efficient pyramid squeeze attention block on convolutional neural network[M]. Computer vision‒ACCV 2022, 13843, 13728-13737(2023).

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Yunhao Li, Chengtie Li, Qiuming Li. A Lightweight Real‐Time Weld Defect Classification Model[J]. Chinese Journal of Lasers, 2025, 52(12): 1202105

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