{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T02:24:31Z","timestamp":1769912671398,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T00:00:00Z","timestamp":1684886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52101398"],"award-info":[{"award-number":["52101398"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Strengthening regulations on carbon emissions from ships is important for ensuring that China can achieve its dual carbon aims of reaching peak carbon emissions before 2030 and achieving carbon neutrality before 2060. Currently, the primary means of monitoring ship exhaust emissions are the sniffing method and non-imaging optical remote sensing; however, these methods suffer from a low prediction efficiency and high cost. We developed a method for predicting the CO2 content of ship exhaust that uses a convolutional neural network and mid-infrared spectral images. First, a bench experiment was performed to synchronously obtain mid-wave infrared spectral images of the ship exhaust plume and true values for the CO2 concentration from the online monitoring of eight spectral channels. Then, the ResNet50 residual neural network, which is suitable for image prediction tasks, was selected to predict the CO2 content. The preprocessed mid-infrared spectral image of each channel and the corresponding true value for the CO2 content were input to the neural network, and convolution was applied to extract the radiation characteristics. The neural network then mapped the relationship between the true CO2 content and the radiation characteristics for each channel, which it used to predict the CO2 content in the ship exhaust. The results demonstrated that the predicted and true CO2 contents had a root mean square error of &lt;0.2, mean absolute error of &lt;0.15, and mean absolute percentage error of &lt;3.5 for all eight channels. The developed model demonstrated a high prediction accuracy with one channel in particular demonstrating the best performance. This study demonstrates that the method used for predicting the CO2 content of ship exhaust based on convolutional neural networks and mid-infrared spectral images is feasible and has reference significance for the remote monitoring of ship exhaust emissions.<\/jats:p>","DOI":"10.3390\/rs15112721","type":"journal-article","created":{"date-parts":[[2023,5,25]],"date-time":"2023-05-25T02:00:55Z","timestamp":1684980055000},"page":"2721","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Using a Convolutional Neural Network and Mid-Infrared Spectral Images to Predict the Carbon Dioxide Content of Ship Exhaust"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhenduo","family":"Zhang","sequence":"first","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Huijie","family":"Wang","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}]},{"given":"Kai","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of AI & Robotics, Academy for Engineering & Technology, Fudan University, Shanghai 200433, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"Navigation College, Dalian Maritime University, Dalian 116026, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"key":"ref_1","unstructured":"Hockstad, L., and Hanel, L. (2023, May 21). Inventory of US Greenhouse Gas Emissions and Sinks; Environmental System Science Data Infrastructure for a Virtual Ecosystem, Available online: https:\/\/www.osti.gov\/dataexplorer\/biblio\/dataset\/1464240)."},{"key":"ref_2","unstructured":"Central Committee of the Communist Party of China, and State Council (2021). Chinese Enterprise Reform and Development 2021 Blue Book, China Commerce and Trade Press."},{"key":"ref_3","unstructured":"IMO (2021). Fourth IMO GHG Study 2020 Full Report. Int. Marit. Organ., 6, 951\u2013952."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4735","DOI":"10.1016\/j.atmosenv.2009.04.059","article-title":"Transport impacts on atmosphere and climate: Shipping","volume":"44","author":"Eyring","year":"2010","journal-title":"Atmos. Env."},{"key":"ref_5","unstructured":"UNCTAD (2021). Review of Maritime Report 2021, United Nations Publications."},{"key":"ref_6","unstructured":"United Nations (2021). Trade and Development Report 2021, from Recovery Resilience: The Development Dimension Overview, United Nations."},{"key":"ref_7","unstructured":"IMO (International Maritime Organization) (2021). Guidelines on the Operational Carbon Intensity Reduction Factors Relative to Reference Lines (CII Reduction Factors Guidelines, G3), MEPC."},{"key":"ref_8","unstructured":"Beecken, J. (2015). Remote Measurements of Gas and Particulate Matter Emissions from Individual Ships. [Ph.D. Dissertation, Chalmers Tekniska Hogskola]."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"149","DOI":"10.5194\/amt-7-149-2014","article-title":"Mobile measurements of ship emissions in two harbour areas in Finland","volume":"7","author":"Pirjola","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"110980","DOI":"10.1016\/j.oceaneng.2022.110980","article-title":"Ship emission monitoring sensor web for research and application","volume":"249","author":"Fan","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"10087","DOI":"10.5194\/acp-15-10087-2015","article-title":"Monitoring compliance with sulfur content regulations of shipping fuel by in situ measurements of ship emissions","volume":"15","author":"Kattner","year":"2015","journal-title":"Atmos. Chem. Phys."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"140885","DOI":"10.1016\/j.scitotenv.2020.140885","article-title":"Protocol development for real-time ship fuel sulfur content determination using drone based plume sniffing microsensor system","volume":"744","author":"Anand","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"102128","DOI":"10.1016\/j.tre.2020.102128","article-title":"Synergistic path planning of multi-UAVs for air pollution detection of ships in ports","volume":"144","author":"Shen","year":"2020","journal-title":"Trans. Res. E-Log."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2597","DOI":"10.5194\/amt-7-2597-2014","article-title":"Field test of available methods to measure remotely SOx and NOx emissions from ships","volume":"7","author":"Alfoldy","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gagnon, J.P., Larivi\u00e8re-Bastien, M., Thibodeau, J., and Tombet, S.B. (2021, January 24\u201326). Remote estimation of sulfur content in fuel from SO2 and CO2 quantification of ship exhaust plumes. Proceedings of the 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), Amsterdam, The Netherlands.","DOI":"10.1109\/WHISPERS52202.2021.9484059"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101479","DOI":"10.1016\/j.apr.2022.101479","article-title":"An improved method for optimizing detection bands of marine exhaust SO2 concentration in ultraviolet dual-band measurements based on signal-to-noise ratio","volume":"13","author":"Zhang","year":"2022","journal-title":"Atmos. Pollut. Res."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"114484","DOI":"10.1016\/j.marpolbul.2022.114484","article-title":"Development of a spectrum-based ship fuel sulfur content real-time evaluation method","volume":"188","author":"Hao","year":"2023","journal-title":"Mar. Pollut. Bull."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"128359","DOI":"10.1016\/j.optcom.2022.128359","article-title":"Simultaneous detection of multiple gaseous pollutants using multi-wavelength differential absorption LIDAR","volume":"518","author":"Yang","year":"2022","journal-title":"Opt. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103631","DOI":"10.1016\/j.bspc.2022.103631","article-title":"A hybrid deep CNN-Cov-19-Res-Net Transfer learning architype for an enhanced brain tumor detection and classification scheme in medical image processing","volume":"76","author":"Kumar","year":"2022","journal-title":"Biomed. Signal Process. Control."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100719","DOI":"10.1016\/j.measen.2023.100719","article-title":"Smart office automation via faster R-CNN based face recognition and internet of things","volume":"27","author":"Rajeshkumar","year":"2023","journal-title":"Meas. Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.neunet.2022.09.001","article-title":"HVIOnet: A deep learning based hybrid visual\u2013inertial odometry approach for unmanned aerial system position estimation","volume":"155","author":"Muhammet","year":"2022","journal-title":"Neural Netw."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., 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_23","doi-asserted-by":"crossref","first-page":"115066","DOI":"10.1016\/j.engstruct.2022.115066","article-title":"Torsional capacity evaluation of RC beams using an improved bird swarm algorithm optimised 2D convolutional neural network","volume":"273","author":"Yu","year":"2022","journal-title":"Eng. Struct."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"100128","DOI":"10.1016\/j.dibe.2023.100128","article-title":"Automated damage diagnosis of concrete jack arch beam using optimized deep stacked autoencoders and multi-sensor fusion","volume":"14","author":"Yu","year":"2023","journal-title":"Dev. Built. Environ."},{"key":"ref_25","unstructured":"Peter, J.M., Robert, O.K., and Jonathan, G. (2023). Field Measurements for Passive Environmental Remote Sensing, Elsevier."},{"key":"ref_26","first-page":"102912","article-title":"Object detection from UAV thermal infrared images and videos using YOLO models","volume":"112","author":"Jiang","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","unstructured":"Ji, D.-C., and Min, Y.-K. (2022). A Sensor Fusion System with Thermal Infrared Camera and LiDAR for Autonomous Vehicles and Deep Learning Based Object Detection, ICT Express."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2807","DOI":"10.5194\/amt-7-2807-2014","article-title":"Retrieval of sulfur dioxide from a ground-based thermal infrared imaging camera","volume":"7","author":"Prata","year":"2014","journal-title":"Atmos. Meas. Tech."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"104661","DOI":"10.1016\/j.infrared.2023.104661","article-title":"Convolutional neural network assisted infrared imaging technology: An enhanced online processing state monitoring method for laser powder bed fusion","volume":"131","author":"Jiacheng","year":"2023","journal-title":"Infrared Phys. Technol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1039","DOI":"10.1016\/j.jmrt.2023.01.075","article-title":"Prediction of variable-groove weld penetration using texture features of infrared thermal images and machine learning methods","volume":"23","author":"Rongwei","year":"2023","journal-title":"J. Mater. Res. Technol."},{"key":"ref_31","unstructured":"Tombet, S.B., Gatti, S., Eisele, A., and Morton, V. (2020, January 4\u20138). Observation and quantification of CO2 passive degassing at sulphur banks from Kilauea Volcano using thermal infrared multispectral imaging. Proceedings of the Copernicus Meetings, Online."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Platt, U., Bobrowski, N., and Butz, A. (2018). Ground-based remote sensing and imaging of volcanic gases and quantitative determination of multi-species emission fluxes. Geosciences, 8.","DOI":"10.3390\/geosciences8020044"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"116501","DOI":"10.1016\/j.envpol.2021.116501","article-title":"Ship fuel sulfur content prediction based on convolutional neural network and ultraviolet camera images","volume":"273","author":"Kai","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"117698","DOI":"10.1016\/j.envpol.2021.117698","article-title":"Surveillance of ship emissions and fuel sulfur content based on imaging detection and multi-task deep learning","volume":"288","author":"Kai","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hashim, M., Ng, H.L., Zakari, D.M., Sani, D.A., Chindo, M.M., Hassan, N., Azmy, M.M., and Pour, A.B. (2023). Mapping of greenhouse gas concentration in Peninsular Malaysia Industrial Areas using unmanned aerial vehicle-based sniffer sensor. Remote Sens., 15.","DOI":"10.3390\/rs15010255"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Siozos, P., Psyllakis, G., and Velegrakis, M. (2023). Remote operation of an open-path, laser-based instrument for atmospheric CO2 and CH4 monitoring. Photonics, 10.","DOI":"10.3390\/photonics10040386"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2721\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:40:56Z","timestamp":1760125256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/11\/2721"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,24]]},"references-count":36,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["rs15112721"],"URL":"https:\/\/doi.org\/10.3390\/rs15112721","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,24]]}}}