激光与光电子学进展, 2021, 58 (4): 0410016, 网络出版: 2021-02-24   

基于改进生成对抗网络和MobileNetV3的带钢缺陷分类 下载: 1013次

Strip Defect Classification Based on Improved Generative Adversarial Networks and MobileNetV3
作者单位
1 西安工程大学机电工程学院, 陕西 西安 710048
2 绍兴市柯桥区西纺纺织产业创新研究院, 浙江 绍兴 312030
3 西安工程大学计算机科学学院, 陕西 西安 710048
引用该论文

常江, 管声启, 师红宇, 胡璐萍, 倪奕棋. 基于改进生成对抗网络和MobileNetV3的带钢缺陷分类[J]. 激光与光电子学进展, 2021, 58(4): 0410016.

Jiang Chang, Shengqi Guan, Hongyu Shi, Luping Hu, Yiqi Ni. Strip Defect Classification Based on Improved Generative Adversarial Networks and MobileNetV3[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410016.

参考文献

[1] Ghorai S, Mukherjee A, Gangadaran M, et al. Automatic defect detection on hot-rolled flat steel products[J]. IEEE Transactions on Instrumentation and Measurement, 2013, 62(3): 612-621.

[2] Neogi N, Mohanta D K, Dutta P K. Review of vision-based steel surface inspection systems[J]. EURASIP Journal on Image and Video Processing, 2014, 2014(1): 1-19.

[3] SaitoK, UshikuY, HaradaT, et al.Strong-weak distribution alignment for adaptive object detection[C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA.New York: IEEE Press, 2019: 6949- 6958.

[4] YangF, FanH, ChuP, et al.Clustered object detection in aerial images[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South).New York: IEEE Press, 2019: 8310- 8319.

[5] Yan ZY, Yuan YC, Zuo WM, et al.Perspective-guided convolution networks for crowd counting[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South).New York: IEEE Press, 2019: 952- 961.

[6] 沈晓海, 栗泽昊, 李敏, 等. 基于多任务深度学习的铝材表面缺陷检测[J]. 激光与光电子学进展, 2020, 57(10): 101501.

    Shen X H, Li Z H, Li M, et al. Aluminum surface-defect detection based on multi-task deep learning[J]. Laser & Optoelectronics Progress, 2020, 57(10): 101501.

[7] 张广世, 葛广英, 朱荣华, 等. 基于改进YOLOv3网络的齿轮缺陷检测[J]. 激光与光电子学进展, 2020, 57(12): 121009.

    Zhang G S, Ge G Y, Zhu R H, et al. Gear defect detection based on the improved YOLOv3 network[J]. Laser & Optoelectronics Progress, 2020, 57(12): 121009.

[8] 刘芳, 吴志威, 杨安喆, 等. 基于多尺度特征融合的自适应无人机目标检测[J]. 光学学报, 2020, 40(10): 1015002.

    Liu F, Wu Z W, Yang A Z, et al. Multi-scale feature fusion based adaptive object detection for UAV[J]. Acta Optica Sinica, 2020, 40(10): 1015002.

[9] BengioY, DelalleauO. On the expressive power of deep architectures[M] ∥Elomaa T, Hollmén J, Mannila H, et al. Discovery Science. DS 2011. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2011, 6926: 1.

[10] VannocciM, RitaccoA, CastellanoA, et al. Flatness defect detection and classification in hot rolled steel strips using convolutional neural networks[C]∥15th International Work Conference on Artificial Neural Networks(IWANN),June 12-14,2019,Gran Canaria, Spain. Cham: Springer, 2019: 220- 234.

[11] 王立中, 管声启. 基于深度学习算法的带钢表面缺陷识别[J]. 西安工程大学学报, 2017, 31(5): 669-674.

    Wang L Z, Guan S Q. Strip steel surface defect recognition based on deep learning[J]. Journal of Xi'an Polytechnic University, 2017, 31(5): 669-674.

[12] Mi ZS, Song YH, YanY. A defect classification network based on deformation dense connection in wire rod surface image[C]∥2019 2nd China Symposium on Cognitive Computing and Hybrid Intelligence (CCHI), September 21-22, 2019, Xi'an, China.New York: IEEE Press, 2019: 155- 160.

[13] Liu S Y, Guo H Y, Hu J G, et al. A novel data augmentation scheme for pedestrian detection with attribute preserving GAN[J]. Neurocomputing, 2020, 401: 123-132.

[14] GoodfellowI, Pouget-AbadieJ, MirzaM, et al.Generative adversarial nets[C]∥27th International Conference on Neural Information Processing Systems(NIPS), December 8-13, 2014, Montreal, Canada.Cambridge: MIT Press, 2014: 2672- 2680.

[15] MirzaM, OsinderoS. Conditional generative adversarial nets[EB/OL]. [2020-06-20].https:∥arxiv.org/abs/1411. 1784.

[16] RadfordA, MetzL, ChintalaS. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL]. [2020-06-24].https:∥arxiv.org/abs/1511. 06434.

[17] Xuan Q, Chen Z Z, Liu Y, et al. Multiview generative adversarial network and its application in pearl classification[J]. IEEE Transactions on Industrial Electronics, 2019, 66(10): 8244-8252.

[18] Yi C, Cho J. Improving the performance of multimedia pedestrian classification with images synthesized using a deep convolutional generative adversarial network[J]. Multimedia Tools and Applications, 2020, 89: 1-16.

[19] OdenaA, OlahC, ShlensJ. Conditional image synthesis with auxiliary classifier GANs[EB/OL]. [2020-06-21].https:∥arxiv.org/abs/1610. 09585.

[20] HowardA, SandlerM, ChenB, et al.Searching for MobileNetV3[C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South).New York: IEEE Press, 2019: 1314- 1324.

[21] Song K C, Yan Y H. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects[J]. Applied Surface Science, 2013, 285: 858-864.

常江, 管声启, 师红宇, 胡璐萍, 倪奕棋. 基于改进生成对抗网络和MobileNetV3的带钢缺陷分类[J]. 激光与光电子学进展, 2021, 58(4): 0410016. Jiang Chang, Shengqi Guan, Hongyu Shi, Luping Hu, Yiqi Ni. Strip Defect Classification Based on Improved Generative Adversarial Networks and MobileNetV3[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410016.

本文已被 6 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!