{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:40:18Z","timestamp":1773931218848,"version":"3.50.1"},"reference-count":24,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,3,15]],"date-time":"2022-03-15T00:00:00Z","timestamp":1647302400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"H2020","award":["371883374"],"award-info":[{"award-number":["371883374"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Maritime operations rely heavily on surveillance and require reliable and timely data that can inform decisions and planning. Critical information in such cases includes the exact location of objects in the water, such as vessels, persons, and others. Due to the unique characteristics of the maritime environment, the location of even inert objects changes through time, depending on the weather conditions, water currents, etc. Unmanned aerial vehicles (UAVs) can be used to support maritime operations by providing live video streams and images from the area of operations. Machine learning algorithms can be developed, trained, and used to automatically detect and track objects of specific types and characteristics. EFFECTOR is an EU-funded project, developing an Interoperability Framework for maritime surveillance. Within the project, we developed an embedded system that employs machine learning algorithms, allowing a UAV to autonomously detect objects in the water and keep track of their changing position through time. Using the on-board computation unit of the UAV, we ran and present the results of a series of comparative tests among possible architecture sizes and training datasets for the detection and tracking of objects in the maritime environment. We tested architectures based on their efficiency, accuracy, and speed. A combined solution for training the datasets is suggested, providing optimal efficiency and accuracy.<\/jats:p>","DOI":"10.3390\/computation10030042","type":"journal-article","created":{"date-parts":[[2022,3,16]],"date-time":"2022-03-16T03:34:13Z","timestamp":1647401653000},"page":"42","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A Comparative Study of Autonomous Object Detection Algorithms in the Maritime Environment Using a UAV Platform"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0208-0337","authenticated-orcid":false,"given":"Emmanuel","family":"Vasilopoulos","sequence":"first","affiliation":[{"name":"Institute of Communication and Computer Systems (ICCS), 15773 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2029-8409","authenticated-orcid":false,"given":"Georgios","family":"Vosinakis","sequence":"additional","affiliation":[{"name":"Institute of Communication and Computer Systems (ICCS), 15773 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Krommyda","sequence":"additional","affiliation":[{"name":"Institute of Communication and Computer Systems (ICCS), 15773 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9148-6258","authenticated-orcid":false,"given":"Lazaros","family":"Karagiannidis","sequence":"additional","affiliation":[{"name":"Institute of Communication and Computer Systems (ICCS), 15773 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5078-3248","authenticated-orcid":false,"given":"Eleftherios","family":"Ouzounoglou","sequence":"additional","affiliation":[{"name":"Institute of Communication and Computer Systems (ICCS), 15773 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4089-1990","authenticated-orcid":false,"given":"Angelos","family":"Amditis","sequence":"additional","affiliation":[{"name":"Institute of Communication and Computer Systems (ICCS), 15773 Athens, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1093\/ia\/iiz146","article-title":"Regional maritime security in the eastern Mediterranean: Expectations and reality","volume":"95","author":"Rubin","year":"2019","journal-title":"Int. Aff."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.apor.2011.01.005","article-title":"Wind-induced drift of objects at sea: The leeway field method","volume":"33","author":"Breivik","year":"2011","journal-title":"Appl. Ocean. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.rse.2004.10.002","article-title":"Computer-based identification and tracking of Antarctic icebergs in SAR images","volume":"94","author":"Silva","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Eiselt, H., and Marianov, V. (2015). Vessel Location Modeling for Maritime Search and Rescue. Applications of Location Analysis, Springer. International Series in Operations Research & Management Science.","DOI":"10.1007\/978-3-319-20282-2"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"B\u00fcrkle, A., and Essendorfer, B. (2010, January 3\u20135). Maritime surveillance with integrated systems. Proceedings of the 2010 International WaterSide Security Conference, Carrara, Italy.","DOI":"10.1109\/WSSC.2010.5730231"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Agbissoh OTOTE, D., Li, B., Ai, B., Gao, S., Xu, J., Chen, X., and Lv, G. (2019). A Decision-Making Algorithm for Maritime Search and Rescue Plan. Sustainability, 11.","DOI":"10.3390\/su11072084"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"104447","DOI":"10.1016\/j.marpol.2021.104447","article-title":"Maritime autonomous vehicles and international laws on boat migration: Lessons from the use of drones in the Mediterranean","volume":"127","author":"Klein","year":"2021","journal-title":"Mar. Policy"},{"key":"ref_8","unstructured":"Mart\u00ednez, F.X., Castells, M., Mart\u00edn, M., and Puente, J.M. (2020). Advantages and disadvantages of some unmanned aerial vehicles deployed in maritime surveillance. Maritime Transport VIII: Proceedings of the 8th International Conference on Maritime Transport: Technology, Innovation and Research: Maritime Transport\u201920, Departament de Ci\u00e8ncia i Enginyeria N\u00e0utiques, Universitat Polit\u00e8cnica de Catalunya. Available online: http:\/\/hdl.handle.net\/2117\/329709."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Maltezos, E., Douklias, A., Dadoukis, A., Misichroni, F., Karagiannidis, L., Antonopoulos, M., Voulgary, K., Ouzounoglou, E., and Amditis, A. (2021). The INUS Platform: A Modular Solution for Object Detection and Tracking from UAVs and Terrestrial Surveillance Assets. Computation, 9.","DOI":"10.3390\/computation9020012"},{"key":"ref_10","unstructured":"(2022, January 18). EFFECTOR Homepage. Available online: https:\/\/www.effector-project.eu\/."},{"key":"ref_11","unstructured":"(2022, January 18). CISE Homepage. Available online: https:\/\/ec.europa.eu\/oceans-and-fisheries\/ocean\/blue-economy\/other-sectors\/common-information-sharing-environment-cise_en."},{"key":"ref_12","unstructured":"(2022, January 18). YOLOv5 Repository. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 14\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Nepal, U., and Eslamiat, H. (2022). Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs. Sensors, 22.","DOI":"10.3390\/s22020464"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Liao, H.-Y.M., Wu, Y.-H., Chen, P.-Y., Hsieh, J.-W., and Yeh, I.-H. (2020, January 14\u201319). CSPNet: A New Backbone that can Enhance Learning Capability of CNN. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA.","DOI":"10.1109\/CVPRW50498.2020.00203"},{"key":"ref_16","unstructured":"Bochkovskiy, A., Wang, C.-Y., and Liao, H. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Hosang, J., Benenson, R., and Schiele, B. (2017, January 21\u201326). Learning Non-maximum Suppression. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.685"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_19","unstructured":"(2022, January 18). Seagull Dataset. Available online: https:\/\/vislab.isr.tecnico.ulisboa.pt\/seagull-dataset\/."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2720","DOI":"10.1109\/TCSVT.2017.2775524","article-title":"A Data Set for Airborne Maritime Surveillance Environments","volume":"29","author":"Ribeiro","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_21","unstructured":"(2022, January 18). COCO Homepage. Available online: https:\/\/cocodataset.org\/#home."},{"key":"ref_22","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_24","unstructured":"(2022, January 18). VOC Homepage. Available online: http:\/\/host.robots.ox.ac.uk\/pascal\/VOC\/."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/10\/3\/42\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:37:04Z","timestamp":1760135824000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/10\/3\/42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,15]]},"references-count":24,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["computation10030042"],"URL":"https:\/\/doi.org\/10.3390\/computation10030042","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,15]]}}}