{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:25:21Z","timestamp":1780615521267,"version":"3.54.1"},"reference-count":37,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2023,10,8]],"date-time":"2023-10-08T00:00:00Z","timestamp":1696723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["42102326"],"award-info":[{"award-number":["42102326"]}]},{"name":"National Natural Science Foundation of China","award":["2022Q05"],"award-info":[{"award-number":["2022Q05"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QD073"],"award-info":[{"award-number":["ZR2020QD073"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2022QD042"],"award-info":[{"award-number":["ZR2022QD042"]}]},{"name":"Basic Scientific Fund for National Public Research Institutes of China","award":["42102326"],"award-info":[{"award-number":["42102326"]}]},{"name":"Basic Scientific Fund for National Public Research Institutes of China","award":["2022Q05"],"award-info":[{"award-number":["2022Q05"]}]},{"name":"Basic Scientific Fund for National Public Research Institutes of China","award":["ZR2020QD073"],"award-info":[{"award-number":["ZR2020QD073"]}]},{"name":"Basic Scientific Fund for National Public Research Institutes of China","award":["ZR2022QD042"],"award-info":[{"award-number":["ZR2022QD042"]}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["42102326"],"award-info":[{"award-number":["42102326"]}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["2022Q05"],"award-info":[{"award-number":["2022Q05"]}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["ZR2020QD073"],"award-info":[{"award-number":["ZR2020QD073"]}]},{"name":"Shandong Provincial Natural Science Foundation, China","award":["ZR2022QD042"],"award-info":[{"award-number":["ZR2022QD042"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This study introduces a novel approach to the critical task of submarine pipeline or cable (POC) detection by employing GoogleNet for the automatic recognition of side-scan sonar (SSS) images. The traditional interpretation methods, heavily reliant on human interpretation, are replaced with a more reliable deep-learning-based methodology. We explored the enhancement of model accuracy via transfer learning and scrutinized the influence of three distinct pre-training datasets on the model\u2019s performance. The results indicate that GoogleNet facilitated effective identification, with accuracy and precision rates exceeding 90%. Furthermore, pre-training with the ImageNet dataset increased prediction accuracy by about 10% compared to the model without pre-training. The model\u2019s prediction ability was best promoted by pre-training datasets in the following order: Marine-PULSE \u2265 ImageNet &gt; SeabedObjects-KLSG. Our study shows that pre-training dataset categories, dataset volume, and data consistency with predicted data are crucial factors affecting pre-training outcomes. These findings set the stage for future research on automatic pipeline detection using deep learning techniques and emphasize the significance of suitable pre-training dataset selection for CNN models.<\/jats:p>","DOI":"10.3390\/rs15194873","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T04:56:43Z","timestamp":1696827403000},"page":"4873","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Revealing the Potential of Deep Learning for Detecting Submarine Pipelines in Side-Scan Sonar Images: An Investigation of Pre-Training Datasets"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0959-9016","authenticated-orcid":false,"given":"Xing","family":"Du","sequence":"first","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources of the People\u2019s Republic of China, Qingdao 266061, China"},{"name":"College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongfu","family":"Sun","sequence":"additional","affiliation":[{"name":"National Deep Sea Center, Qingdao 266237, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yupeng","family":"Song","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources of the People\u2019s Republic of China, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lifeng","family":"Dong","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources of the People\u2019s Republic of China, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolong","family":"Zhao","sequence":"additional","affiliation":[{"name":"First Institute of Oceanography, Ministry of Natural Resources of the People\u2019s Republic of China, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2146853","DOI":"10.1080\/08839514.2022.2146853","article-title":"Deep Learning Approach For Objects Detection in Underwater Pipeline Images","volume":"36","author":"Lerga","year":"2022","journal-title":"Appl. Artif. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s12555-019-0691-3","article-title":"Realistic Sonar Image Simulation Using Deep Learning for Underwater Object Detection","volume":"18","author":"Sung","year":"2020","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1007\/s11001-020-09417-7","article-title":"Image Feature Extraction Based on Improved FCN for UUV Side-Scan Sonar","volume":"41","author":"Wang","year":"2020","journal-title":"Mar. Geophys. Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10091","DOI":"10.1007\/s11042-022-12054-4","article-title":"A Novel Sonar Target Detection and Classification Algorithm","volume":"81","author":"Fan","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"14889","DOI":"10.1038\/s41598-021-94266-6","article-title":"A Multi-Hazard Map-Based Flooding, Gully Erosion, Forest Fires, and Earthquakes in Iran","volume":"11","author":"Pouyan","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1038\/s41597-023-01955-0","article-title":"All-Hazards Dataset Mined from the US National Incident Management System 1999\u20132020","volume":"10","author":"Short","year":"2023","journal-title":"Sci. Data"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhao, C., and Lu, Z. (2018). Remote Sensing of Landslides\u2014A Review. Remote Sens., 10.","DOI":"10.3390\/rs10020279"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7793","DOI":"10.1038\/s41467-022-35418-8","article-title":"Seismic Multi-Hazard and Impact Estimation via Causal Inference from Satellite Imagery","volume":"13","author":"Xu","year":"2022","journal-title":"Nat. Commun."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"104692","DOI":"10.1016\/j.envsoft.2020.104692","article-title":"Building a Landslide Hazard Indicator with Machine Learning and Land Surface Models","volume":"129","author":"Stanley","year":"2020","journal-title":"Environ. Model. Softw."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103858","DOI":"10.1016\/j.earscirev.2021.103858","article-title":"Deep Learning for Geological Hazards Analysis: Data, Models, Applications, and Opportunities","volume":"223","author":"Ma","year":"2021","journal-title":"Earth-Sci. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3952","DOI":"10.1038\/s41467-020-17591-w","article-title":"Earthquake Transformer\u2014An Attentive Deep-Learning Model for Simultaneous Earthquake Detection and Phase Picking","volume":"11","author":"Mousavi","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101131","DOI":"10.1016\/j.sandf.2022.101131","article-title":"Evaluation and Updating of Ishihara\u2019s (1985) Model for Liquefaction Surface Expression, with Insights from Machine and Deep Learning","volume":"62","author":"Rateria","year":"2022","journal-title":"Soils Found."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1007\/s12517-021-07156-6","article-title":"Landslide Susceptibility Investigation for Idukki District of Kerala Using Regression Analysis and Machine Learning","volume":"14","author":"Jones","year":"2021","journal-title":"Arab. J. Geosci."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Chang, Z., Du, Z., Zhang, F., Huang, F., Chen, J., Li, W., and Guo, Z. (2020). Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models. Remote Sens., 12.","DOI":"10.3390\/rs12030502"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1007\/s12517-019-5012-x","article-title":"Seismic Hazard and Risk Assessment: A Review of State-of-the-Art Traditional and GIS Models","volume":"13","author":"Jena","year":"2020","journal-title":"Arab. J. Geosci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Du, X., Sun, Y., Song, Y., Xiu, Z., and Su, Z. (2022). Submarine Landslide Susceptibility and Spatial Distribution Using Different Unsupervised Machine Learning Models. Appl. Sci., 12.","DOI":"10.3390\/app122010544"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1744","DOI":"10.1121\/1.5067734","article-title":"Using Machine Learning in Ocean Noise Analysis during Marine Seismic Reflection Surveys","volume":"144","author":"Abadi","year":"2018","journal-title":"J. Acoust. Soc. Am."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3263","DOI":"10.1016\/j.matpr.2021.07.222","article-title":"Side Scan Sonar Image Augmentation for Sediment Classification Using Deep Learning Based Transfer Learning Approach","volume":"80","author":"Chandrashekar","year":"2023","journal-title":"Mater. Today Proc."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ishigami, G., and Yoshida, K. (2021). Proceedings of the Field and Service Robotics, Springer.","DOI":"10.1007\/978-981-15-9460-1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106599","DOI":"10.1016\/j.margeo.2021.106599","article-title":"Integration of Machine Learning Using Hydroacoustic Techniques and Sediment Sampling to Refine Substrate Description in the Western Cape, South Africa","volume":"440","author":"Pillay","year":"2021","journal-title":"Mar. Geol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"103936","DOI":"10.1016\/j.oregeorev.2020.103936","article-title":"Deep Learning of Terrain Morphology and Pattern Discovery via Network-Based Representational Similarity Analysis for Deep-Sea Mineral Exploration","volume":"129","author":"Juliani","year":"2021","journal-title":"Ore Geol. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106332","DOI":"10.1016\/j.margeo.2020.106332","article-title":"Characterisation of Seafloor Substrate Using Advanced Processing of Multibeam Bathymetry, Backscatter, and Sidescan Sonar in Table Bay, South Africa","volume":"429","author":"Pillay","year":"2020","journal-title":"Mar. Geol."},{"key":"ref_23","first-page":"379","article-title":"Application of Machine Learning and Artificial Intelligence in Oil and Gas Industry","volume":"6","author":"Sircar","year":"2021","journal-title":"Pet. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"125522","DOI":"10.1109\/ACCESS.2019.2939005","article-title":"Accurate Underwater ATR in Forward-Looking Sonar Imagery Using Deep Convolutional Neural Networks","volume":"7","author":"Jin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"173450","DOI":"10.1109\/ACCESS.2020.3024813","article-title":"Shipwreck Target Recognition in Side-Scan Sonar Images by Improved YOLOv3 Model Based on Transfer Learning","volume":"8","author":"Yulin","year":"2020","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhu, B., Wang, X., Chu, Z., Yang, Y., and Shi, J. (2019). Active Learning for Recognition of Shipwreck Target in Side-Scan Sonar Image. Remote Sens., 11.","DOI":"10.3390\/rs11030243"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1007\/s13349-022-00654-5","article-title":"An Ensemble Method for Automatic Real-Time Detection, Evaluation and Position of Exposed Subsea Pipelines Based on 3D Real-Time Sonar System","volume":"13","author":"Xiong","year":"2023","journal-title":"J. Civil Struct. Health Monit."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yan, J., Meng, J., and Zhao, J. (2021). Bottom Detection from Backscatter Data of Conventional Side Scan Sonars through 1D-UNet. Remote Sens., 13.","DOI":"10.3390\/rs13051024"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sun, Y., Zheng, H., Zhang, G., Ren, J., Xu, H., and Xu, C. (2022). DP-ViT: A Dual-Path Vision Transformer for Real-Time Sonar Target Detection. Remote Sens., 14.","DOI":"10.3390\/rs14225807"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Du, X., Sun, Y., Song, Y., Sun, H., and Yang, L. (2023). A Comparative Study of Different CNN Models and Transfer Learning Effect for Underwater Object Classification in Side-Scan Sonar Images. Remote Sens., 15.","DOI":"10.3390\/rs15030593"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"47407","DOI":"10.1109\/ACCESS.2020.2978880","article-title":"Underwater Object Classification in Sidescan Sonar Images Using Deep Transfer Learning and Semisynthetic Training Data","volume":"8","author":"Huo","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2014). Going Deeper with Convolutions. arXiv.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_33","unstructured":"Bousmalis, K., Trigeorgis, G., Silberman, N., Krishnan, D., and Erhan, D. (2016). Domain Separation Networks. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Pires de Lima, R., and Marfurt, K. (2020). Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis. Remote Sens., 12.","DOI":"10.3390\/rs12010086"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Rostami, M., Kolouri, S., Eaton, E., and Kim, K. (2019). Deep Transfer Learning for Few-Shot SAR Image Classification. Remote Sens., 11.","DOI":"10.20944\/preprints201905.0030.v1"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Koga, Y., Miyazaki, H., and Shibasaki, R. (2020). A Method for Vehicle Detection in High-Resolution Satellite Images That Uses a Region-Based Object Detector and Unsupervised Domain Adaptation. Remote Sens., 12.","DOI":"10.3390\/rs12030575"},{"key":"ref_37","unstructured":"Du, X. (2023). Side-Scan Sonar Images of Marine Engineering Geology (Marine_PULSE Dataset), Zenodo."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4873\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:03:03Z","timestamp":1760130183000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/19\/4873"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,8]]},"references-count":37,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["rs15194873"],"URL":"https:\/\/doi.org\/10.3390\/rs15194873","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,8]]}}}