Construction of convolutional neural network model for micro-scale bump on metal pipe fittings

Liu Zihao; Tao Guohao; Xue Feng; Lu Yebo; Yang Jun

doi:10.12086/oee.2025.240275

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Liu Z H, Tao G H, Xue F, et al. Construction of convolutional neural network model for micro-scale bump on metal pipe fittings[J]. Opto-Electron Eng, 2025, 52(3): 240275. doi: 10.12086/oee.2025.240275

Citation:

Liu Z H, Tao G H, Xue F, et al. Construction of convolutional neural network model for micro-scale bump on metal pipe fittings[J]. Opto-Electron Eng, 2025, 52(3): 240275. doi: 10.12086/oee.2025.240275

Construction of convolutional neural network model for micro-scale bump on metal pipe fittings

1.
College of Mechanical Engineering, Tianjin University, Tianjin 300072, China
2.
College of Artificial Intelligence, Jiaxing University, Jiaxing, Zhejiang 314100, China
3.
School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
4.
Zhejiang Master Hydraulic Fittings Co., Ltd., Jiaxing, Zhejiang 316002, China
5.
School of Mechanical Engineering, Jiaxing University, Jiaxing, Zhejiang 314100, China

Fund Project: National Natural Science Foundation of China (62374074), Zhejiang Province Pioneer and Leading Geese Plan (2024C04028), the Public Welfare Research Project of Jiaxing City (2024AY10059, 2024AD10045, 2024AY40010), the School-enterprise cooperation project (00523144), the Key Research and Development Project of Haiyan (2024ZD03), and Qin Shen BackBone Scholar Program of Jiaxing University (CD70623008)

More Information

^*Corresponding author: juneryoung@zjxu.edu.cn
CSTR: 32245.14.oee.2025.240275

Received Date 26 November 2024

Revised Date 15 February 2025

Accepted Date 17 February 2025

Published Date 28 March 2025

Abstract

Abstract

The low detection rate of tiny defects on the surface of metal pipe fittings is a key issue confronting industrial component inspection. In aiming at this problem, an improved YOLOv9-MM model was constructed to improve the accuracy of small target detection. A real-time image acquisition system for precision metal pipe fittings was designed. By using an annular light source combined with a telecentric lens, the surface of pipe fittings can be snapped by the CCD camera and covered at all angles to eliminate the problem of missing areas. The feature map extracted methods of shallow network were introduced, and the upper sampling module of Dysample was combined to realize the dynamic fusion of depth features. By improving the loss function, the precision of small target detection is greatly improved. The results show that the proposed method has an average detection accuracy of 70.2% and a detection speed of 90 f/s. The proposed method shows some feasibility in the actual application.
- metal fittings /
- small defect detection /
- deep learning /
- YOLOv9 /
- detection system

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References

[1]	伍麟, 郝鸿宇, 宋友. 基于计算机视觉的工业金属表面缺陷检测综述[J]. 自动化学报, 2024, 50(7): 1261−1283. doi: 10.16383/j.aas.c230039 CrossRef Google Scholar Wu L, Hao H Y, Song Y. A review of metal surface defect detection based on computer vision[J]. Acta Autom Sin, 2024, 50(7): 1261−1283. doi: 10.16383/j.aas.c230039 CrossRef Google Scholar
[2]	高亦沁, 罗智宇, 王一超, 等. 面向国产超算的操作系统评测与优化[J/OL]. 计算机科学, 2024.http://kns.cnki.net/kcms/detail/50.1075.tp.20240925.1330.007.html Google Scholar Gao Y Q, Luo Z Y, Wang Y C, et al. Evaluation and optimization of operating system for domestic supercomputer[J/OL]. Comput Sci, 2024.http://kns.cnki.net/kcms/detail/50.1075.tp.20240925.1330.007.html Google Scholar
[3]	Rajitha B, Tiwari A, Agarwal S. A new local homogeneity analysis method based on pixel intensities for image defect detection[C]//2015 IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), Kolkata, India, 2015: 200–206. https://doi.org/10.1109/ReTIS.2015.7232878. Google Scholar
[4]	Chen W, Zou B, Yang J Z, et al. The machined surface defect detection of improved superpixel segmentation and two-level region aggregation based on machine vision[J]. J Manuf Process, 2022, 80: 287−301. doi: 10.1016/j.jmapro.2022.05.038 CrossRef Google Scholar
[5]	Huang Z R. Fusion of complex networks-based global and local features for feature representation[C]//2021 International Conference on Machine Learning and Cybernetics (ICMLC), Adelaide, Australia, 2021: 1–6. https://doi.org/10.1109/ICMLC54886.2021.9737154. Google Scholar
[6]	Sun Z J, Wang X H, Luo F, et al. Scale adaptive attention network for accurate defect detection from metal parts[J]. IEEE Access, 2024, 12: 131035−131043. doi: 10.1109/ACCESS.2024.3432660 CrossRef Google Scholar
[7]	Zhu X F, Wang Q M, Zhang B F, et al. An improved feature enhancement CenterNet model for small object defect detection on metal surfaces[J]. Adv Theory Simul, 2024, 7(8): 2301230. doi: 10.1002/adts.202301230 CrossRef Google Scholar
[8]	Dong H W, Song K C, He Y, et al. PGA-Net: pyramid feature fusion and global context attention network for automated surface defect detection[J]. IEEE Trans Industr Inform, 2020, 16(12): 7448−7458. doi: 10.1109/TII.2019.2958826 CrossRef Google Scholar
[9]	Peng T, Zheng Y, Zhao L, et al. Industrial product surface anomaly detection with realistic synthetic anomalies based on defect map prediction[J]. Sensors, 2024, 24(1): 264. doi: 10.3390/s24010264 CrossRef Google Scholar
[10]	Xie X, Xu L, Li X L, et al. A high-effective multitask surface defect detection method based on CBAM and atrous convolution[J]. J Adv Mech Des Syst Manuf, 2022, 16(6): JAMDSM0063. doi: 10.1299/jamdsm.2022jamdsm0063 CrossRef Google Scholar
[11]	Xue P, Jiang C H, Pang H L. Detection of various types of metal surface defects based on image processing[J]. Trait Signal, 2021, 38(4): 1071−1078. doi: 10.18280/ts.380417 CrossRef Google Scholar
[12]	Chen T, Saxena S, Li L L, et al. Pix2seq: a language modeling framework for object detection[C]//The Tenth International Conference on Learning Representations, 2021. Google Scholar
[13]	Wang C Y, Yeh I H, Liao H Y M. YOLOv9: learning what you want to learn using programmable gradient information[C]//18th European Conference on Computer Vision, Milan, Italy, 2024: 1–21. https://doi.org/10.1007/978-3-031-72751-1_1. Google Scholar
[14]	Lin T Y, Dollár P, Girshick R, et al. Feature pyramid networks for object detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017: 936–944. https://doi.org/10.1109/CVPR.2017.106. Google Scholar
[15]	Li S, Wang Z C, Zhang X D, et al. Robust image steganography against general downsampling operations with lossless secret recovery[J]. IEEE Trans Dependable Secure Comput, 2024, 21(1): 340−352. doi: 10.1109/TDSC.2023.3253691 CrossRef Google Scholar
[16]	Liu S, Qi L, Qin H F, et al. Path aggregation network for instance segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018: 8759–8768. https://doi.org/10.1109/CVPR.2018.00913. Google Scholar
[17]	Lin Y, Tu Y, Dou Z. An improved neural network pruning technology for automatic modulation classification in edge devices[J]. IEEE Trans Veh Technol, 2020, 69(5): 5703−5706. doi: 10.1109/TVT.2020.2983143 CrossRef Google Scholar
[18]	Liu W Z, Lu H, Fu H T, et al. Learning to upsample by learning to sample[C]//2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 2023: 6004–6014. https://doi.org/10.1109/ICCV51070.2023.00554. Google Scholar
[19]	Ma S L, Xu Y. MPDIoU: a loss for efficient and accurate bounding box regression[Z]. arXiv: 2307.07662, 2023. https://arxiv.org/abs/2307.07662. Google Scholar
[20]	Zhang H, Xu C, Zhang S J. Inner-IoU: more effective intersection over union loss with auxiliary bounding box[Z]. arXiv: 2311.02877, 2023. https://arxiv.org/abs/2311.02877. Google Scholar
[21]	郭从洲, 李可, 李贺, 等. 遥感图像质量等级分类的深度卷积神经网络方法[J]. 武汉大学学报(信息科学版), 2022, 47(8): 1279−1286. doi: 10.13203/j.whugis20200292 CrossRef Google Scholar Guo C Z, Li K, Li H, et al. Deep convolution neural network method for remote sensing image quality level classification[J]. Geomatics Inf Sci Wuhan Univ, 2022, 47(8): 1279−1286. doi: 10.13203/j.whugis20200292 CrossRef Google Scholar
[22]	陶国好, 刘子豪, 徐晓萌, 等. 圆柱金属管件外表面图像在线采集装置构建及关键参数控制优化[J]. 计量学报, 2024, 45(10): 1494−1501. doi: 10.3969/j.issn.1000-1158.2024.10.09 CrossRef Google Scholar Tao G H, Liu Z H, Xu X M, et al. Construction of on-line image acquisition device for cylindrical metal pipe fittings and control optimization of key parameters[J]. J Metrol, 2024, 45(10): 1494−1501. doi: 10.3969/j.issn.1000-1158.2024.10.09 CrossRef Google Scholar
[23]	Ren S Q, He K M, Sun R J. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Trans Pattern Anal Mach Intell, 2017, 39(6): 1137−1149. doi: 10.1109/TPAMI.2016.2577031 CrossRef Google Scholar
[24]	Wang C Y, Bochkovskiy A, Liao H Y M. YOLOv7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada, 2023: 7464–7475. https://doi.org/10.1109/CVPR52729.2023.00721. Google Scholar
[25]	Varghese R, M S. YOLOv8: a novel object detection algorithm with enhanced performance and robustness[C]//2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), Chennai, India, 2024: 1–6. https://doi.org/10.1109/ADICS58448.2024.10533619. Google Scholar
[26]	Wang A, Chen H, Liu L H, et al. YOLOv10: real-time end-to-end object detection[C]//Proceedings of the 38th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, 2024. Google Scholar
[27]	Tian Z, Shen C H, Chen H, et al. FCOS: fully convolutional one-stage object detection[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019: 9626–9635. https://doi.org/10.1109/ICCV.2019.00972. Google Scholar
[28]	Feng B X, Cai J P. PCB defect detection via local detail and global dependency information[J]. Sensors, 2023, 23(18): 7755. doi: 10.3390/s23187755 CrossRef Google Scholar

Overview

Overview

Metal pipe fittings are also known as "industrial food". The type of metal connection component plays an extremely important role in key national military equipment, such as spacecraft and nuclear industry. Tiny surface defects existing on the surface of these metal pipe fittings can directly affect the safety and quality of the target. Currently, supercomputers are widely used in scientific computation, climate simulation, artificial intelligence, nuclear bomb explosions, aerospace, and other fields and have huge requirements for safety and reliability. The study objective of this paper-metal pipe fittings are mainly applied in the liquid cooling plates of GPU clusters in the supercomputers. Introducing fluorine-containing cooling liquid into the interior of the fittings can quickly cool the high-temperature and high-heat integrated boards. If there are defects such as knocks, or scratches, cracks on the surface of the metal fittings, due to the pressure difference inside and outside, the liquid will be leaked out, causing tremendous economic losses upon the GPU chips. Therefore, to investigate the impact of surface defects on metal fittings, which can significantly affect the safety and stability of electro-mechanical equipment assembly, an improved detection algorithm called YOLOv9-MM is proposed. Firstly, a real-time image acquisition system for precision metal pipe fittings was designed. The ring light source combined with a telecentric lens can achieve full angle coverage of the surface of pipe fittings and eliminate the problem of missing areas. Secondly, based on deep convolutional neural network construction theory, a module called MCDown was constructed to combine max pooling and segmentation-connection strategy, thereby enhancing feature extraction capability and effectively reducing spatial dimensions. Additionally, shallow network feature maps were incorporated to enrich the feature information of small targets, and a dysample upsampling module was utilized to achieve the dynamic fusion of deep features. Finally, the combination of MDPIoU (mean distance of prediction intersection over union) and InnerIoU regression loss optimization strategies can accelerate model convergence and improve small target detection accuracy. Experimental results indicate that the average detection accuracy reaches 69.7%, a 3.9% improvement over the baseline model, with a detection speed of 90 f/s. Compared to other mainstream object detection algorithms, the proposed method demonstrates a better balance between precision and detection speed, showing its feasibility and value in practical applications. Furthermore, the model's performance in different environments and datasets also shows good robustness and generalization ability. Future work will expand upon other metal surface micro-defect image datasets and continuously optimize the algorithm to further improve its detection accuracy and generalization ability.