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12 December 2024 Improved object detection networks for internal oil and gas pipeline inspection
Chuang Liang, Qiangzheng Jing, Xumiao Lv, Runkun Lu, Chang Liu, Debiao Li, Jinzhong Chen
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Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134392S (2024) https://doi.org/10.1117/12.3055450
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China
Abstract
The primary methods for oil and gas pipeline defect identification currently rely on acoustic and magnetic techniques, with visual solutions remaining scarce. However, recent artificial intelligence (AI) advancements suggest promising avenues for real-time visual defect detection. This research explores the capabilities of AI for real-time detection and aims to refine and optimize this methodology. We achieve this by implementing widely utilized deep learning neural networks, such as YOLO and DETR, for training and processing on-site video images. Our model demonstrates a recognition accuracy exceeding 87% through subsequent application testing, indicating significant potential for real-world implementation in oil and gas pipeline defect detection.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Chuang Liang, Qiangzheng Jing, Xumiao Lv, Runkun Lu, Chang Liu, Debiao Li, and Jinzhong Chen "Improved object detection networks for internal oil and gas pipeline inspection", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134392S (12 December 2024); https://doi.org/10.1117/12.3055450
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KEYWORDS
Object detection
Inspection
Defect detection
Target detection
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