Spatial Attention Enhanced Pipeline Inner Surface Defect Detection and Digital Twin Modelling
Pages 289 - 294
Abstract
Defect detection of inner defects of pipelines holds significant importance. Our investigation on previous works has indicated that most vision based defect detection methods can not deliver satisfactory results due to complexity of the problem. In this work, a new method termed Sim-RTMDet. Our proposal is empowered to better capture detailed features of the defect regions, thereby improving detection performance. Additionally, for better visualization of the detection results, we created a digital twin virtual model of the inner surface inspection of the pipeline. The experimental results demonstrate significant improvements in both detection performance and digital-twin accuracy, thereby validating the effectiveness of our proposed approach.
References
[1]
Wael A Altabey, Sallam A Kouritem, Mohammed I Abouheaf, and Nabil Nahas. 2022. A deep learning-based approach for pipeline cracks monitoring. In 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 1–6.
[2]
Wael A Altabey, Sallam A Kouritem, Mohammed I Abouheaf, and Nabil Nahas. 2022. Research in image processing for pipeline crack detection applications. In 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 1–6.
[3]
Tao Chen, Qing Du, Weizhong Li, Shuiping Sheng, and Hong Zhou. 2023. Ultrasonic Imaging Detection of Welding Joint Defects of Pressure Pipeline Based on Phased Array Technology. In 2023 International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII). IEEE, 375–379.
[4]
Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun. 2021. Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021).
[5]
Jungeun Kim, Keunho Choi, Gunwoo Kim, and Yongmoo Suh. 2012. Classification cost: An empirical comparison among traditional classifier, Cost-Sensitive Classifier, and MetaCost. Expert Systems with Applications 39, 4 (2012), 4013–4019.
[6]
Xiang Li, Wenhai Wang, Lijun Wu, Shuo Chen, Xiaolin Hu, Jun Li, Jinhui Tang, and Jian Yang. 2020. Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection. Advances in Neural Information Processing Systems 33 (2020), 21002–21012.
[7]
Chengqi Lyu, Wenwei Zhang, Haian Huang, Yue Zhou, Yudong Wang, Yanyi Liu, Shilong Zhang, and Kai Chen. 2022. Rtmdet: An empirical study of designing real-time object detectors. arXiv preprint arXiv:2212.07784 (2022).
[8]
Francesco Mattia, Giuseppe Satalino, Laura Dente, and Guido Pasquariello. 2006. Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions. IEEE transactions on geoscience and remote sensing 44, 4 (2006), 900–912.
[9]
Shunsuke Moritsuka and Tohru Kamiya. 2021. A classification method for magnetic particle testing image using U-net. In 2021 21st International Conference on Control, Automation and Systems (ICCAS). IEEE, 2047–2050.
[10]
Chao Peng, Xiangyu Zhang, Gang Yu, Guiming Luo, and Jian Sun. 2017. Large kernel matters–improve semantic segmentation by global convolutional network. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4353–4361.
[11]
Yuzhou Shi, Yuanxiang Li, Xian Wei, and Yongjun Zhou. 2017. A faster-rcnn based chemical fiber paper tube defect detection method. In 2017 5th international conference on enterprise systems (ES). IEEE, 173–177.
[12]
Harold S Stone and L Williams. 2008. On the uniqueness of the convolution theorem for the Fourier transform. NEC Labs. Amer. Princeton, NJ. Online: http://citeseer. ist. psu. edu/176038. html, accessed on 19 (2008).
[13]
Peize Sun, Rufeng Zhang, Yi Jiang, Tao Kong, Chenfeng Xu, Wei Zhan, Masayoshi Tomizuka, Lei Li, Zehuan Yuan, Changhu Wang, 2021. Sparse r-cnn: End-to-end object detection with learnable proposals. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 14454–14463.
[14]
Alexandre Vimont, Henri Leleu, and Isabelle Durand-Zaleski. 2022. Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France. The European Journal of Health Economics 23, 2 (2022), 211–223.
[15]
Yuanyuan Wang, Chao Wang, Hong Zhang, Yingbo Dong, and Sisi Wei. 2019. Automatic ship detection based on RetinaNet using multi-resolution Gaofen-3 imagery. Remote Sensing 11, 5 (2019), 531.
[16]
Lingxiao Yang, Ru-Yuan Zhang, Lida Li, and Xiaohua Xie. 2021. Simam: A simple, parameter-free attention module for convolutional neural networks. In International conference on machine learning. PMLR, 11863–11874.
[17]
Jian Zhang, Chunyu Li, and Li Sheng. 2023. Transfer Learning-Based Defect Detection for Natural Gas Pipelines. In 2023 42nd Chinese Control Conference (CCC). IEEE, 4979–4984.
[18]
Zhaohui Zheng, Ping Wang, Wei Liu, Jinze Li, Rongguang Ye, and Dongwei Ren. 2020. Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 12993–13000.
[19]
Hai-Jian Zhong, Zhang-Wei Ling, Cun-Jian Miao, Wei-Can Guo, and Ping Tang. 2017. A new robot-based system for in-pipe ultrasonic inspection of pressure pipelines. In 2017 Far East NDT New Technology & Application Forum (FENDT). IEEE, 246–250.
Index Terms
- Spatial Attention Enhanced Pipeline Inner Surface Defect Detection and Digital Twin Modelling
Recommendations
Visual Analytics Approach for Crane Anomaly Detection Based on Digital Twin
Cooperative Design, Visualization, and EngineeringAbstractAnomaly detection of crane operating status is the basis for ensuring its stable operation. The current detection method based on anomaly detection algorithms cannot clearly distinguish normal data from abnormal data. And the high dimensions of ...
Split-Path Enhanced Pipeline Scheduling
Software pipelining increases the loop execution throughput by overlapping the execution of successive iterations in a pipelined fashion. For loops with control flows, however, software pipelining is not straightforward because we need to consider the ...
Digital twin for credit card fraud detection: opportunities, challenges, and fraud detection advancements
AbstractCredit cards are widely used for payments due to their convenience and broad acceptance. Their popularity comes with the critical challenge of safeguarding personal and payment information from fraud and unauthorized access. Robust security ...
Highlights- Explore credit card fraud risk factors and current fraud detection techniques.
- Address fraud challenges with digital twin implementation.
- Explore digital twin in credit card fraud detection: advantages & role.
- Explain the flow ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
December 2023
371 pages
ISBN:9798400709203
DOI:10.1145/3639631
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 16 February 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ACAI 2023
ACAI 2023: 2023 6th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2023
Sanya, China
Acceptance Rates
Overall Acceptance Rate 173 of 395 submissions, 44%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 23Total Downloads
- Downloads (Last 12 months)23
- Downloads (Last 6 weeks)7
Reflects downloads up to 14 Dec 2024
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format