Citation: | DENG Zhichao, YAN Runming, YANG Huitong, CHEN Haolin, LAI Jinxiang, LEI Liang. Multiview solder joint defect detection based on improved ResNet[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(3): 56-62. DOI: 10.12073/j.hjxb.20210928004 |
Su S, Akkara F J, Thaper R, et al. A state-of-the-art review of fatigue life prediction models for solder joint[J]. Journal of Electronic Packaging, 2019, 141(4): 040802. doi: 10.1115/1.4043405
|
Wang Q, Liu X, Liu W, et al. MetaSearch: incremental product search via deep meta-learning[J]. IEEE Transactions on Image Processing, 2020, 29: 7549-7564.
|
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, America, 2016: 770-778.
|
Zhang Z, Wen G, Chen S. Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding[J]. Journal of Manufacturing Processes, 2019, 45: 208 − 216.
|
王中任, 王小刚, 刘德政, 等. 基于焊枪轮廓特征提取的焊接偏差测定方法[J]. 焊接学报, 2020, 41(7): 59 − 64. doi: 10.12073/j.hjxb.20191026002
Wang Zhongren, Wang Xiaogang, Liu Dezheng, et al. Welding eviation measurement method based on welding torch contour feature extraction[J]. Transactions of the China Welding Institution, 2020, 41(7): 59 − 64. doi: 10.12073/j.hjxb.20191026002
|
肖文波, 何银水, 袁海涛, 等. 镀锌钢GMAW焊缝成形特征与焊枪方向同步实时检测[J]. 焊接学报, 2021, 42(12): 78 − 82. doi: 10.12073/j.hjxb.20201021001
Xiao Wenbo, He Yinshui, Yuan Haitao, et al. Synchronous real-time detection of weld bead geometry and the welding torch in galvanized steel GAMW[J]. Transactions of the China Welding Institution, 2021, 42(12): 78 − 82. doi: 10.12073/j.hjxb.20201021001
|
Ding R, Dai L, Li G, et al. TDD-net: a tiny defect detection network for printed circuit boards[J]. CAAI Transactions on Intelligence Technology, 2019, 4(2): 110 − 116. doi: 10.1049/trit.2019.0019
|
Chi Dazhao, Gang Tie. Defect detection method based on 2D entropy image segmentation[J]. China Welding, 2020, 29(1): 45 − 49.
|
王睿, 胡云雷, 刘卫朋, 等. 基于边缘AI的焊缝X射线图像缺陷检测[J]. 焊接学报, 2022, 43(1): 79 − 84.
Wang Rui, Hu Yunlei, Liu Weipeng, et al. Defect detection of weld X-ray image based on edge AI[J]. Transactions of the China Welding Institution, 2022, 43(1): 79 − 84.
|
Veit A, Wilber M J, Belongie S. Residual networks behave like ensembles of relatively shallow networks[J]. Advances in Neural Information Processing Systems, 2016, 29: 550 − 558.
|
Ma C, Guo Y, Yang J, et al. Learning multi-view representation with LSTM for 3D shape recognition and retrieval[J]. IEEE Transactions on Multimedia, 2018, 21(5): 1169 − 1182.
|
迟大钊, 李孙珏, 孙昌立, 等. 基于双目视觉的缺陷深度测量方法[J]. 焊接学报, 2016, 37(11): 7 − 10.
Chi Dazhao, Li Sunjue, Sun Changli, et al. Binocular visual based defect buried depth testing method[J]. Transactions of the China Welding Institution, 2016, 37(11): 7 − 10.
|
Jiao Y, Jermsittiparsert K, Krasnopevtsev A Y, et al. Interaction of thermal cycling and electric current on reliability of solder joints in different solder balls[J]. Materials Research Express, 2019, 6(10): 106302. doi: 10.1088/2053-1591/ab366d
|
Dai W, Mujeeb A, Erdt M, et al. Soldering defect detection in automatic optical inspection[J]. Advanced Engineering Informatics, 2020, 43: 101004. doi: 10.1016/j.aei.2019.101004
|
Liu Z, Cao Y, Li Y, et al. Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network[J]. Computer Methods and Programs in Biomedicine, 2020, 187: 105019. doi: 10.3390/info11020125
|
He K, Fan H, Wu Y, et al. Momentum contrast for unsupervised visual representation learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA, 2020: 9729 − 9738.
|
Yu F, Wang D, Shelhamer E, et al. Deep layer aggregation[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, USA, 2018: 2403 − 2412.
|
Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. Salt Lake City, USA, 2018: 7132 − 7141.
|
[1] | 2022-7 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(7): 1-8. |
[2] | 2021-11 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(11): 1-6. |
[3] | 2020-12 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(12): 98-103. |
[4] | 2020-8 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(8): 97-102. |
[5] | 2020-5 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(5): 97-102. |
[6] | 2020-4 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(4): 97-102. |
[7] | 2020-3 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(3): 97-102. |
[8] | 2020-2 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(2): 97-102. |
[9] | 2020-1 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(1): 97-102. |
[10] | 2019-3 Abstract[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(3): 161-168. |