Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems
The integrity of submarine pipelines and cables is crucial for safeguarding marine oil, gas, and information transmission, as well as ecological security. Employing automated identification of side-scan sonar (SSS) images can enhance marine geophysical survey efficiency, enabling high-frequency asse...
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| Format: | Article |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1596238/full |
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| author | Min Wei Min Wei Yongqing Yu Xing Du Yupeng Song Lifeng Dong Qikun Zhou Linfeng Wang Longying Zhang Yamei Wang |
| author_facet | Min Wei Min Wei Yongqing Yu Xing Du Yupeng Song Lifeng Dong Qikun Zhou Linfeng Wang Longying Zhang Yamei Wang |
| author_sort | Min Wei |
| collection | DOAJ |
| description | The integrity of submarine pipelines and cables is crucial for safeguarding marine oil, gas, and information transmission, as well as ecological security. Employing automated identification of side-scan sonar (SSS) images can enhance marine geophysical survey efficiency, enabling high-frequency assessment of seabed anthropogenic footprints. However, there is a notable gap in research regarding the comparative performance of different models and the impact of data expansion. This study presents an in-depth comparison of various convolutional neural network (CNN) models-specifically, AlexNet, GoogleNet, and VGG-16-focusing on their prediction accuracy and computational efficiency in analyzing SSS datasets. Our findings reveal that GoogleNet outperforms the others, offering superior prediction accuracy with balanced computational demands. While AlexNet is less accurate, it is beneficial for scenarios with limited computational resources. Conversely, VGG-16 shows comparatively weaker performance, making it less suitable for SSS image analysis. Notably, data expansion significantly influences model accuracy, although its impact varies across different models. This research contributes critical insights into model selection for marine geological applications, demonstrating the potential of intelligent interpretation systems in modern marine geology. |
| format | Article |
| id | doaj-art-0b37b9460dc746d082d2415683797ca6 |
| institution | OA Journals |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-0b37b9460dc746d082d2415683797ca62025-08-20T02:35:57ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-06-011310.3389/feart.2025.15962381596238Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systemsMin Wei0Min Wei1Yongqing Yu2Xing Du3Yupeng Song4Lifeng Dong5Qikun Zhou6Linfeng Wang7Longying Zhang8Yamei Wang9College of Marine Geosciences, Ocean University of China, Qingdao, ChinaMarine Oil Production Plant, Shengli Oilfield Company, SINOPEC, Dongying, ChinaMarine Oil Production Plant, Shengli Oilfield Company, SINOPEC, Dongying, ChinaFirst Institute of Oceanography, Ministry of Natural Resources of the People’s Republic of China, Qingdao, ChinaFirst Institute of Oceanography, Ministry of Natural Resources of the People’s Republic of China, Qingdao, ChinaFirst Institute of Oceanography, Ministry of Natural Resources of the People’s Republic of China, Qingdao, ChinaFirst Institute of Oceanography, Ministry of Natural Resources of the People’s Republic of China, Qingdao, ChinaMarine Oil Production Plant, Shengli Oilfield Company, SINOPEC, Dongying, ChinaMarine Oil Production Plant, Shengli Oilfield Company, SINOPEC, Dongying, ChinaMarine Oil Production Plant, Shengli Oilfield Company, SINOPEC, Dongying, ChinaThe integrity of submarine pipelines and cables is crucial for safeguarding marine oil, gas, and information transmission, as well as ecological security. Employing automated identification of side-scan sonar (SSS) images can enhance marine geophysical survey efficiency, enabling high-frequency assessment of seabed anthropogenic footprints. However, there is a notable gap in research regarding the comparative performance of different models and the impact of data expansion. This study presents an in-depth comparison of various convolutional neural network (CNN) models-specifically, AlexNet, GoogleNet, and VGG-16-focusing on their prediction accuracy and computational efficiency in analyzing SSS datasets. Our findings reveal that GoogleNet outperforms the others, offering superior prediction accuracy with balanced computational demands. While AlexNet is less accurate, it is beneficial for scenarios with limited computational resources. Conversely, VGG-16 shows comparatively weaker performance, making it less suitable for SSS image analysis. Notably, data expansion significantly influences model accuracy, although its impact varies across different models. This research contributes critical insights into model selection for marine geological applications, demonstrating the potential of intelligent interpretation systems in modern marine geology.https://www.frontiersin.org/articles/10.3389/feart.2025.1596238/fullmarine geophysical monitoringseabed anthropogenic featuresintelligent earth observationsonar image interpretationcoastal zone management |
| spellingShingle | Min Wei Min Wei Yongqing Yu Xing Du Yupeng Song Lifeng Dong Qikun Zhou Linfeng Wang Longying Zhang Yamei Wang Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems Frontiers in Earth Science marine geophysical monitoring seabed anthropogenic features intelligent earth observation sonar image interpretation coastal zone management |
| title | Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems |
| title_full | Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems |
| title_fullStr | Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems |
| title_full_unstemmed | Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems |
| title_short | Automated detection of submarine pipelines in the Yellow River Estuary: a deep learning approach for side-scan sonar data in dynamic deltaic systems |
| title_sort | automated detection of submarine pipelines in the yellow river estuary a deep learning approach for side scan sonar data in dynamic deltaic systems |
| topic | marine geophysical monitoring seabed anthropogenic features intelligent earth observation sonar image interpretation coastal zone management |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1596238/full |
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