{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T17:45:19Z","timestamp":1776275119023,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,12]],"date-time":"2022-06-12T00:00:00Z","timestamp":1654992000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"University of Malaga"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the evolution of the convolutional neural network (CNN), object detection in the underwater environment has gained a lot of attention. However, due to the complex nature of the underwater environment, generic CNN-based object detectors still face challenges in underwater object detection. These challenges include image blurring, texture distortion, color shift, and scale variation, which result in low precision and recall rates. To tackle this challenge, we propose a detection refinement algorithm based on spatial\u2013temporal analysis to improve the performance of generic detectors by suppressing the false positives and recovering the missed detections in underwater videos. In the proposed work, we use state-of-the-art deep neural networks such as Inception, ResNet50, and ResNet101 to automatically classify and detect the Norway lobster Nephrops norvegicus burrows from underwater videos. Nephrops is one of the most important commercial species in Northeast Atlantic waters, and it lives in burrow systems that it builds itself on muddy bottoms. To evaluate the performance of proposed framework, we collected the data from the Gulf of Cadiz. From experiment results, we demonstrate that the proposed framework effectively suppresses false positives and recovers missed detections obtained from generic detectors. The mean average precision (mAP) gained a 10% increase with the proposed refinement technique.<\/jats:p>","DOI":"10.3390\/s22124441","type":"journal-article","created":{"date-parts":[[2022,6,13]],"date-time":"2022-06-13T02:01:44Z","timestamp":1655085704000},"page":"4441","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Novel Detection Refinement Technique for Accurate Identification of Nephrops norvegicus Burrows in Underwater Imagery"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5444-9637","authenticated-orcid":false,"given":"Atif","family":"Naseer","sequence":"first","affiliation":[{"name":"ETSI Telecomunicaci\u00f3n, Universidad de M\u00e1laga, 29071 Malaga, Spain"},{"name":"Science and Technology Unit, Umm al Qura University, Makkah 21955, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7817-6442","authenticated-orcid":false,"given":"Enrique Nava","family":"Baro","sequence":"additional","affiliation":[{"name":"ETSI Telecomunicaci\u00f3n, Universidad de M\u00e1laga, 29071 Malaga, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7406-8441","authenticated-orcid":false,"given":"Sultan Daud","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National University of Technology, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yolanda","family":"Vila","sequence":"additional","affiliation":[{"name":"Centro Oceanogr\u00e1fico de C\u00e1diz (IEO-CSIC), Instituto Espa\u00f1ol de Oceanograf\u00eda, 11006 C\u00e1diz, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101088","DOI":"10.1016\/j.ecoinf.2020.101088","article-title":"Fish detection and species classification in underwater environments using deep learning with temporal information","volume":"Volume 57","author":"Jalal","year":"2020","journal-title":"Ecological Informatics"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.1093\/icesjms\/fsw118","article-title":"Image-based seabed classification: What can we learn from terrestrial remote sensing?","volume":"73","author":"Diesing","year":"2016","journal-title":"ICES J. 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