Defect detection of weld X-ray image based on edge AI

WANG Rui; HU Yunlei; LIU Weipeng; LI Haitao

doi:10.12073/j.hjxb.20210516001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2022 > 43(1): 79-84. > DOI: 10.12073/j.hjxb.20210516001

WANG Rui, HU Yunlei, LIU Weipeng, LI Haitao. Defect detection of weld X-ray image based on edge AI[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 79-84. DOI: 10.12073/j.hjxb.20210516001

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Defect detection of weld X-ray image based on edge AI

WANG Rui^{1, 2,},
HU Yunlei^1, ,,
LIU Weipeng¹,
LI Haitao¹

1.
Hebei University of Technology, Tianjin, 300131, China
2.
State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, Harbin, 150001, China

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Received Date: May 15, 2021
Accepted Date: January 27, 2022
Available Online: January 27, 2022

Graphical Abstract

Abstract

Abstract

In order to improve the practicability of deep learning in X-ray weld defect detection and reduce the hardware requirements of defect detection task, the YOLO-M network with merely 3.6 M parameters is proposed. The amount of network calculation is reduced by introducing a lightweight inverse residual structure into the network; the multi-scale prediction mechanism is used to predict different defect characteristics by network layer; the positive sample information of the image is amplified across the grid to speed up the convergence speed in the process of network training. The network is not only applied to traditional computers, but also tested in the ultra-low power edge artificial intelligence chip Kanzhi K210. The results show that the detection accuracy of the proposed method is 93.5% and the detection speed is 11 fps. This method has good detection accuracy and greatly reduces the cost of defect quality inspection.
- defect detection,
- deep learning,
- lightweight,
- embedded equipment

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References (14)

References

迟大钊, 马子奇, 程怡, 等. 不等厚板搭接焊缝缺陷数字X射线检测[J]. 焊接学报, 2019, 40(11): 45 − 48. doi: 10.12073/j.hjxb.2019400286

Chi Dazhao, Ma Ziqi, Cheng Yi, et al. Digital X-ray detection of lap weld defects in unequal-thickness plates[J]. Transactions of the China Welding Institution, 2019, 40(11): 45 − 48. doi: 10.12073/j.hjxb.2019400286

Yan Z H, Xu H, Huang P F. Multi-scale multi-intensity defect detection in ray image of weld bead[J]. NDT & E International, 2020, 116: 102342 − 102357.

Hou W, Zhang D, Wei Y, et al. Review on computer aided weld defect detection from radiography images[J]. Applied Sciences, 2020, 10(5): 1878 − 1893. doi: 10.3390/app10051878

修延飞, 李海超, 胡广泽, 等. 一种用于穿孔塞焊焊缝特征提取的视觉识别算法[J]. 焊接学报, 2020, 41(2): 75 − 79, 86.

Xiu Yanfei, Li Haichao, Hu Guangze, et al. A visual recognition algorithm for feature extraction of perforated plug weld[J]. Transactions of the China Welding Institution, 2020, 41(2): 75 − 79, 86.

刘涵, 郭润元. 基于X射线图像和卷积神经网络的石油钢管焊缝缺陷检测与识别[J]. 仪器仪表学报, 2018, 39(4): 247 − 256.

Liu Han, Guo Runyuan. Detection and recognition of weld defects of petroleum steel pipe based on X-ray image and convolutional neural network[J]. Chinese Journal of Scientific Instrument, 2018, 39(4): 247 − 256.

樊丁, 胡桉得, 黄健康, 等. 基于改进卷积神经网络的管焊缝X射线图像缺陷识别方法[J]. 焊接学报, 2020, 41(1): 7 − 11.

Fan Ding, Hu Yude, Huang Jiankang, et al. X-ray image defect recognition method of pipe weld based on improved convolutional neural network[J]. Transactions of the China Welding Institution, 2020, 41(1): 7 − 11.

谢经明, 刘默耘, 何文卓, 等. 基于轻量化YOLO的X射线焊缝图像信息检测[J]. 华中科技大学学报(自然科学版), 2021, 49(1): 1 − 5.

Xie Jingming, Liu Moyun, He Wenzhuo, et al. X-ray weld image information detection based on lightweight YOLO[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49(1): 1 − 5.

Ren S, He K, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(6): 1137 − 1149.

鞠默然, 罗海波, 王仲博, 等. 改进的YOLO V3算法及其在小目标检测中的应用[J]. 光学学报, 2019, 39(7): 253 − 260.

Ju Moran, Luo Haibo, Wang Zhongbo, et al. Improved YOLO V3 algorithm and its application in small target detection[J]. Journal of Optics, 2019, 39(7): 253 − 260.

Nacereddine N, Goumeidane A B, Ziou D. Unsupervised weld defect classification in radiographic images using multivariate generalized Gaussian mixture model with exact computation of mean and shape parameters[J]. Computers in Industry, 2019, 108: 132 − 149. doi: 10.1016/j.compind.2019.02.010

Hou D, Miao Z, Xing H, et al. Exploiting low dimensional features from the MobileNets for remote sensing image retrieval[J]. Earth Science Informatics, 2020, 13(4): 1437 − 1443. doi: 10.1007/s12145-020-00484-3

Wang X, Han Y, Wang C, et al. In-Edge AI: Intelligentizing mobile edge computing, caching and communication by federated learning[J]. IEEE Network, 2019, 33(5): 156 − 165. doi: 10.1109/MNET.2019.1800286

Zheng Z, Wang P, Liu W, et al. Distance-IoU loss: Faster and better learning for bounding box regression[C]//Association for the Advancement of Artificial Intelligence. Proceedings of the AAAI Conference on Artificial Intelligence. New York, USA, 2020, 34(7): 12993−13000.

Mery D, Riffo V, Zscherpel U, et al. GDXray: The database of X-ray images for nondestructive testing[J]. Journal of Nondestructive Evaluation, 2015, 34(4): 1 − 12.

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Defect detection of weld X-ray image based on edge AI