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基于产业场景的《深度学习》课程教学改革实践
Practical Teaching Reform of “Deep Learning” Course Based on Industrial Scenarios

DOI: 10.12677/ve.2025.144163, PP. 115-120

Keywords: 应用型本科,工业视觉检测,金属板带材缺陷检测,深度学习
Application-Oriented Undergraduate
, Industrial Visual Inspection, Metal Plate and Strip Defect Detection, Deep Learning

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Abstract:

在应用型本科教育领域,各项教学培养技能应紧密贴合“产业”岗位的实际技能需求,遵循“产业引领教学、产业定义教学、产业驱动教学改革、产业促进教学提升”的发展路径。在工业视觉检测产业领域,金属板带材缺陷检测项目是对学生未来从事金属加工、质量控制、设备维护等工作至关重要。在应用型本科教育中将金属板带材缺陷检测项目知识要点全过程融入到《深度学习》课程教学实践,让学生身临其境地体验工业视觉检测产业场景,旨在提升其技能水平,增强其就业竞争力。
In the field of application-oriented undergraduate, all teaching and training skills should closely meet the actual skills needs of “industry” posts and follow the development path of “industry leading teaching, industry defining teaching, industry driving teaching reform and industry promoting teaching improvement”. In the field of industrial visual inspection, the defect detection project of metal plate and strip is very important for students to engage in metal processing, quality control and equipment maintenance in the future. In application-oriented undergraduate, the whole process of knowledge points of metal plate and strip defect detection project is integrated into the teaching practice of “Deep Learning” course, so that students can experience the industrial scene of industrial visual inspection in an immersive way, aiming at improving their skills and enhancing their employment competitiveness.

References

[1]  王海云. 基于深度卷积神经网络的金属板带材表面缺陷检测研究与应用[D]: [硕士学位论文]. 昆明: 昆明理工大学, 2020
[2]  沙鑫美. 应用型本科教育教学评估的四个基本问题[J]. 高教发展与评估, 2024, 40(3): 1-9.
[3]  李小彤. 基于图像处理和胶囊网络的金属带材表面缺陷检测研究[D]: [硕士学位论文]. 昆明: 昆明理工大学, 2020.
[4]  Obeso, F. and Gonzalez, J. (1997) Intelligent On-Line Surface Inspection on a Skinpass Mill. Iron and Steel Engineer, 74, 29-35.
[5]  萨, 让路, 卢斌. 用安装在火焰切割设备前的涡流探测器检验热连铸板坯的表面质量[J]. 国外钢铁科技, 1995(1): 1-7.
[6]  Kimoto, K., Ueno, S. and Hirose, S. (2006) Image-Based Sizing of Surface-Breaking Cracks by SH-Wave Array Ultrasonic Testing. Ultrasonics, 45, 152-164.
https://doi.org/10.1016/j.ultras.2006.08.006
[7]  石桂芬, 何永辉, 张清. 漏磁法检测薄带钢内部缺陷的研究进展[J]. 世界钢铁, 2013, 13(4): 58-62, 72.
[8]  Zhu, X., Su, W., Lu, L., Li, B., Wang, X. and Dai, J. (2020) Deformable DETR: Deformable Transformers for End-to-End Object Detection. arXiv: 2010.04159.
[9]  Misra, I., Girdhar, R. and Joulin, A. (2021) An End-To-End Transformer Model for 3D Object Detection. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, 10-17 October 2021, 2886-2897.
https://doi.org/10.1109/iccv48922.2021.00290
[10]  Song, K. and Yan, Y. (2013) A Noise Robust Method Based on Completed Local Binary Patterns for Hot-Rolled Steel Strip Surface Defects. Applied Surface Science, 285, 858-864.
https://doi.org/10.1016/j.apsusc.2013.09.002
[11]  Lv, X., Duan, F., Jiang, J., Fu, X. and Gan, L. (2020) Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network. Sensors, 20, Article 1562.
https://doi.org/10.3390/s20061562
[12]  Long, J., Shelhamer, E. and Darrell, T. (2015) Fully Convolutional Networks for Semantic Segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, 7-12 June 2015, 3431-3440.
https://doi.org/10.1109/cvpr.2015.7298965

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