{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T16:30:03Z","timestamp":1775233803726,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,4,2]],"date-time":"2023-04-02T00:00:00Z","timestamp":1680393600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["TED2021-130890B"],"award-info":[{"award-number":["TED2021-130890B"]}]},{"name":"MCIN\/AEI\/10.13039\/501100011033","award":["101086387"],"award-info":[{"award-number":["101086387"]}]},{"name":"European Union NextGenerationEU\/PRTR","award":["TED2021-130890B"],"award-info":[{"award-number":["TED2021-130890B"]}]},{"name":"European Union NextGenerationEU\/PRTR","award":["101086387"],"award-info":[{"award-number":["101086387"]}]},{"name":"HORIZON-MSCA-2021-SE-0","award":["TED2021-130890B"],"award-info":[{"award-number":["TED2021-130890B"]}]},{"name":"HORIZON-MSCA-2021-SE-0","award":["101086387"],"award-info":[{"award-number":["101086387"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Certain fields present significant challenges when attempting to train complex Deep Learning architectures, particularly when the available datasets are limited and imbalanced. Real-time object detection in maritime environments using aerial images is a notable example. Although SeaDronesSee is the most extensive and complete dataset for this task, it suffers from significant class imbalance. To address this issue, we present POSEIDON, a data augmentation tool specifically designed for object detection datasets. Our approach generates new training samples by combining objects and samples from the original training set while utilizing the image metadata to make informed decisions. We evaluate our method using YOLOv5 and YOLOv8 and demonstrate its superiority over other balancing techniques, such as error weighting, by an overall improvement of 2.33% and 4.6%, respectively.<\/jats:p>","DOI":"10.3390\/s23073691","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T02:32:27Z","timestamp":1680489147000},"page":"3691","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["POSEIDON: A Data Augmentation Tool for Small Object Detection Datasets in Maritime Environments"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-6434-2701","authenticated-orcid":false,"given":"Pablo","family":"Ruiz-Ponce","sequence":"first","affiliation":[{"name":"Department of Computer Technology and Computation, University of Alicante, 03690 San Vicente del Raspeig, Spain"}]},{"given":"David","family":"Ortiz-Perez","sequence":"additional","affiliation":[{"name":"Department of Computer Technology and Computation, University of Alicante, 03690 San Vicente del Raspeig, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7798-3055","authenticated-orcid":false,"given":"Jose","family":"Garcia-Rodriguez","sequence":"additional","affiliation":[{"name":"Department of Computer Technology and Computation, University of Alicante, 03690 San Vicente del Raspeig, Spain"}]},{"given":"Benjamin","family":"Kiefer","sequence":"additional","affiliation":[{"name":"Faculty of Science, University of Tuebingen, 72076 Tuebingen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,2]]},"reference":[{"key":"ref_1","first-page":"3212","article-title":"Object Detection with Deep Learning: A Review","volume":"11","author":"Zhao","year":"2018","journal-title":"IEEE Trans. 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