基于深度学习的磁环表面缺陷检测算法
Defect Detection Algorithm of Magnetic Ring Surface Based on Deep Learning
DOI: 10.12677/AIRR.2020.93022, PDF,   
作者: 罗 菁, 王瑞欢：天津工业大学电气工程与自动化学院，天津
关键词: 缺陷检测；深度学习；磁环；YOLOv3；Defect Detection； Deep Learning； Magnetic Ring； YOLOv3
摘要: 在磁环的生产制造过程中，常常由于生产环境、制造工艺等因素，难免会使磁环表面出现各种类型的缺陷。针对传统人工检测低效、耗时、检测精度低的缺点，本文提出了一种基于YOLOv3的磁环表面缺陷检测方法。实验结果表明，YOLOv3的平均识别精度达到了96.19%，单张图片检测速度达到了24.46 ms，该方法在磁环缺陷检测上有一定的先进性和有效性。
Abstract: In the manufacturing process of the magnetic ring, due to the production environment and manu-facturing process factors, it is inevitable that various types of defects may appear. In view of the shortcomings of traditional manual detection, such as low efficiency, time consuming and low ac-curacy, this paper proposes a magnetic ring surface defect detection method based on YOLOv3. Experimental results show that the average recognition accuracy of YOLOv3 has reached 96.19%, and the speed of single image detection has reached 24.46 ms. This method has a certain degree of advancement and effectiveness in the detection of magnetic ring defects.
文章引用：罗菁, 王瑞欢. 基于深度学习的磁环表面缺陷检测算法[J]. 人工智能与机器人研究, 2020, 9(3): 194-200. https://doi.org/10.12677/AIRR.2020.93022

参考文献

[1] 李少波, 杨静, 王铮, 朱书德, 杨观赐. 缺陷检测技术的发展与应用研究综述[J/OL]. 自动化学报, 2020: 1-18.
[2] 李雪琴, 蒋红海, 刘培勇, 殷国富. 非下采样Contourlet域自适应阈值面的磁瓦表面缺陷检测[J]. 计算机辅助设计与图形学学报, 2014, 26(4): 553-558.
[3] 林丽君, 殷鹰, 何明格, 尹湘云. 基于小波模极大值的磁瓦裂纹缺陷边缘检测算法[J]. 电子科技大学学报, 2015, 44(2): 283-288.
[4] 赵恒, 安维胜. 结合深度学习的图像显著目标检测[J]. 激光与光电子学进展, 2018, 55(12): 198-206.
[5] Liu, W., Anguelov, D., Erhan, D., et al. (2016) SSD: Single Shot MultiBox Detector. In: Leibe, B., Matas, J., Sebe, N. and Welling, M., Eds., Computer Vision—ECCV 2016. Springer, Cham, 21-37. [Google Scholar] [CrossRef] 
[6] Redmon, J., Divvala, S., Girshick, R., et al. (2016) You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, 27-30 June 2016, 779-788. [Google Scholar] [CrossRef] 
[7] Redmon, J. and Farhadi, A. (2017) YOLO9000: Better, Faster, Stronger. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, 21-26 July 2017, 6517-6525. [Google Scholar] [CrossRef] 
[8] Redmon, J., Farhadi, A. (2018) YOLOv3: An Incre-mental Improvement.
[9] Girshick, R., Donahue, J., Darrell, T., et al. (2014) Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. 2014 IEEE Conference on Computer Vision & Pattern Recognition. Columbus, 23-28 June 2014, 580-587. [Google Scholar] [CrossRef] 
[10] Girshick, R. (2015) Fast R-CNN. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, 7-13 December 2015, 1440-1448. [Google Scholar] [CrossRef] 
[11] Ren, S., He, K., Girshick, R., et al. (2017) Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Transactions on Pattern Analysis and Machine In-telligence, 39, 1137-1149. [Google Scholar] [CrossRef] 

为你推荐