{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T19:16:38Z","timestamp":1778181398740,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,5,7]],"date-time":"2023-05-07T00:00:00Z","timestamp":1683417600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Technical Testing Center of Shengli Oilfield Branch, China Petroleum &amp; Chemical Corporation","award":["322121"],"award-info":[{"award-number":["322121"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Since the Industrial Revolution, methane has become the second most important greenhouse gas component after CO2 and the second most important culprit of global warming, leading to serious climate change problems such as droughts, fires, floods, and glacial melting. While most of the methane in the atmosphere comes from emissions from energy activities such as petroleum refining, storage tanks are an important source of methane emissions during the extraction and processing of crude oil and natural gas. Therefore, the use of high-resolution remote sensing image data for oil and gas production sites to achieve efficient and accurate statistics for storage tanks is important to promote the strategic goals of \u201ccarbon neutrality and carbon peaking\u201d. Compared with traditional statistical methods for studying oil storage tanks, deep learning-based target detection algorithms are more powerful for multi-scale targets and complex background conditions. In this paper, five deep learning detection algorithms, Faster RCNN, YOLOv5, YOLOv7, RetinaNet and SSD, were selected to conduct experiments on 3568 remote sensing images from five different datasets. The results show that the average accuracy of the Faster RCNN, YOLOv5, YOLOv7 and SSD algorithms is above 0.84, and the F1 scores of YOLOv5, YOLOv7 and SSD algorithms are above 0.80, among which the highest detection accuracy is shown by the SSD algorithm at 0.897 with a high F1 score, while the lowest average accuracy is shown by RetinaNet at only 0.639. The training results of the five algorithms were validated on three images containing differently sized oil storage tanks in complex backgrounds, and the validation results obtained were better, providing more accurate references for practical detection applications in remote sensing of oil storage tank targets in the future.<\/jats:p>","DOI":"10.3390\/rs15092460","type":"journal-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T02:03:31Z","timestamp":1683511411000},"page":"2460","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Comparative Analysis of Remote Sensing Storage Tank Detection Methods Based on Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Lu","family":"Fan","sequence":"first","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"},{"name":"Technical Test Centre of Sinopec, Shengli Oil Field, Dongying 257000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoying","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0839-6738","authenticated-orcid":false,"given":"Yong","family":"Wan","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongshou","family":"Dai","sequence":"additional","affiliation":[{"name":"College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,7]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"2060 China Carbon Neutral-Fossil Energy to Fossil Resource Age","volume":"41","author":"Zhang","year":"2021","journal-title":"Mod. Chem."},{"key":"ref_2","first-page":"100180","article-title":"Technologies and perspectives for achieving carbon neutrality","volume":"2","author":"Wang","year":"2021","journal-title":"Innovation"},{"key":"ref_3","first-page":"1","article-title":"The Scientific Connotation, Realization Path and Challenges of Carbon Neutral Strategy of Carbon Dafeng","volume":"42","author":"Zeng","year":"2022","journal-title":"Mod. Chem."},{"key":"ref_4","first-page":"7","article-title":"China\u2019s Summit Diplomacy and National Green Strategy Capacity Building in the Context of Carbon Neutrality","volume":"36","author":"Xiao","year":"2023","journal-title":"J. Nanjing Univ. Sci. Technol."},{"key":"ref_5","first-page":"52","article-title":"Promote the green development of traditional manufacturing industries in the upgrading and transformation","volume":"440","author":"Jiang","year":"2019","journal-title":"Shanghai Enterp."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1126\/science.1247828","article-title":"Methane on the Rise-Again","volume":"343","author":"Nisbet","year":"2014","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"20018","DOI":"10.1073\/pnas.1314392110","article-title":"Anthropogenicemissions of methane in the United States","volume":"110","author":"Miller","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"3373","DOI":"10.1021\/acs.est.6b05531","article-title":"Assessing the methane emissions from natural gas-fired power plants and oil refineries","volume":"51","author":"Lavoie","year":"2017","journal-title":"Environ. Sci. Technol."},{"key":"ref_9","first-page":"105","article-title":"Key Issues and Recommendations for Methane Emission Control in China","volume":"44","author":"Zhang","year":"2019","journal-title":"Environ. Sustain. Dev."},{"key":"ref_10","first-page":"100193","article-title":"Methane emissions from oil and gas production sites and their storage tanks in West Virginia","volume":"16","author":"Derek","year":"2022","journal-title":"Atmos. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1016\/S1352-2310(00)00423-4","article-title":"Atmospheric concentrations of saturated and aromatic hydrocarbons around a Greek oil refinery","volume":"35","author":"Kalabokas","year":"2001","journal-title":"Atmos. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1080\/10962247.2015.1058304","article-title":"Measured and estimated benzene and volatile organic carbon (VOC)emissions at a major U.S. refinery\/chemical plant: Comparison and prioritization","volume":"65","author":"Hoyt","year":"2015","journal-title":"J. Air Waste Manag. Assoc."},{"key":"ref_13","first-page":"1749","article-title":"A review of optical remote sensing image target detection algorithms","volume":"47","author":"Nie","year":"2021","journal-title":"J. Autom."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wu, Q., Zhang, B., Xu, C., Zhang, H., and Wang, C. (2022). Dense Oil Tank Detection and Classification via YOLOX-TR Network in Large-Scale SAR Images. Remote Sens., 14.","DOI":"10.3390\/rs14143246"},{"key":"ref_15","first-page":"11","article-title":"Advances in deep convolutional neural network-based target detection techniques","volume":"45","author":"Wang","year":"2018","journal-title":"Comput. Sci."},{"key":"ref_16","unstructured":"Yoon, K. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2023.3335484","article-title":"Few-Shot Class-Incremental SAR Target Recognition Based on Hierarchical Embedding and Incremental Evolutionary Network","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.isprsjprs.2020.12.015","article-title":"PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery","volume":"173","author":"Sun","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.isprsjprs.2018.09.014","article-title":"Deep networks under scene-level supervision for multi-class geospatial object detection from remote sensing images","volume":"146","author":"Li","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3991","DOI":"10.1109\/TCYB.2018.2856821","article-title":"DIOD: Fast and efficient weakly semi-supervised deep complex ISAR object detection","volume":"49","author":"Xue","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_21","first-page":"1","article-title":"A new spatial-oriented object detection framework for remote sensing images","volume":"60","author":"Yu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","first-page":"679","article-title":"Weak semantic attention-based remote sensing image interpretable target detection","volume":"49","author":"Zhou","year":"2021","journal-title":"Acta Electron. Sin."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y.J., and Sheng, W.G. (2020). Priority branches for ship detection in optical remote sensing. Remote Sens., 12.","DOI":"10.3390\/rs12071196"},{"key":"ref_24","first-page":"190","article-title":"Research progress of optical remote sensing image target detection based on deep learning","volume":"43","author":"Liao","year":"2022","journal-title":"J. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, X.K., Lyu, S.C., and Wang, X. (2021, January 11\u201317). TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. Proceedings of the International Conference on Computer Vision Workshops, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_26","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., and Erhan, D. (2016, January 11\u201314). SSD: Single shot MultiBox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Lin, T., Goyal, P., and Girshick, R. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., and Darrell, T. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature Pyramid Networks for Object Detection. arXiv.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_34","unstructured":"Li, H.C., Xiong, P.F., An, J., and Wang, L.X. (2018). Pyramid Attention Network for Semantic Segmentation. arXiv."},{"key":"ref_35","unstructured":"Zhang, Z.L., and Mert, R.S. (2018). Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"He, K.M., Zhang, X.Y., Ren, S.Q., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Neubeck, A., and Van, G.L. (2006, January 20\u201324). Efficient Non-Maximum Suppression. Proceedings of the 18th International Conference on Pattern Recognition (ICPR\u201906), Hong Kong, China.","DOI":"10.1109\/ICPR.2006.479"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote Sensing Image Scene Classification: Benchmark and State of the Art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1109\/TGRS.2019.2900302","article-title":"Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","first-page":"1046","article-title":"Aircraft target detection in remote sensing image based on cascade convolution neural network","volume":"48","author":"Yu","year":"2019","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_43","first-page":"53","article-title":"Accuracy comparison and analysis of oil tank detection algorithm based on deep learning remote sensing image","volume":"40","author":"Li","year":"2020","journal-title":"Hydrogr. Surv. Charting"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, F., and Wang, M. (2021). Deep Learning-Based Method for Detection of External Air Conditioner Units from Street View Images. Remote Sens., 13.","DOI":"10.3390\/rs13183691"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2460\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:30:57Z","timestamp":1760124657000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2460"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,7]]},"references-count":44,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092460"],"URL":"https:\/\/doi.org\/10.3390\/rs15092460","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,7]]}}}