TY - GEN
T1 - Autonomous corrosion detection of inside and outside steel pipeline by using YOLO as fast algorithm on image processing
AU - Saragih, Agung Shamsuddin
AU - Aditya, Fernaldy
AU - Ahmed, Waleed K.
N1 - Publisher Copyright:
© 2023 Author(s).
PY - 2023/5/9
Y1 - 2023/5/9
N2 - Maintaining the integrity of pipelines over long distances is an ongoing challenge for oil and gas companies. Among many factors, corrosion is one of the major causes of pipeline failure. Therefore, timely and accurate detection of corrosion is crucial. Along with the growing use of In-Pipe Inspection Robot (IPIR) technologies, a camera-based visual inspection has become a reliable technique for pipe defects detection. However, the conventional manual surveying process by the operators shows a lack of efficiency in this task. This paper studies the application of YOLO, an image-processing algorithm based on Convolutional Neural Network (CNN), for automating corrosion inspection. The results demonstrate that the proposed method is capable of performing detection with an accuracy rate of 64% under AP75 threshold. The system developed can be a promising tool in providing real-time autonomous defect detection to enhance IPIR devices.
AB - Maintaining the integrity of pipelines over long distances is an ongoing challenge for oil and gas companies. Among many factors, corrosion is one of the major causes of pipeline failure. Therefore, timely and accurate detection of corrosion is crucial. Along with the growing use of In-Pipe Inspection Robot (IPIR) technologies, a camera-based visual inspection has become a reliable technique for pipe defects detection. However, the conventional manual surveying process by the operators shows a lack of efficiency in this task. This paper studies the application of YOLO, an image-processing algorithm based on Convolutional Neural Network (CNN), for automating corrosion inspection. The results demonstrate that the proposed method is capable of performing detection with an accuracy rate of 64% under AP75 threshold. The system developed can be a promising tool in providing real-time autonomous defect detection to enhance IPIR devices.
UR - http://www.scopus.com/inward/record.url?scp=85160038964&partnerID=8YFLogxK
U2 - 10.1063/5.0119052
DO - 10.1063/5.0119052
M3 - Conference contribution
AN - SCOPUS:85160038964
T3 - AIP Conference Proceedings
BT - Advances in Metallurgy and Engineering Materials
A2 - Fatriansyah, Jaka Fajar
A2 - Ferdian, Deni
A2 - Putra, Wahyuaji Narottama
A2 - Yuwono, Akhmad Herman
A2 - Dhaneswara, Donanta
A2 - Sofyan, Nofrijon
PB - American Institute of Physics Inc.
T2 - International Meeting on Advances in Metallurgy and Materials 2020, i-MAMM 2020
Y2 - 16 November 2020 through 17 November 2020
ER -