{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T18:16:49Z","timestamp":1789496209652,"version":"build-2803163510"},"reference-count":43,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University","doi-asserted-by":"publisher","award":["PNURSP2025R904"],"award-info":[{"award-number":["PNURSP2025R904"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>The accurate identification of fish species remains a pivotal challenge in aquaculture, as it directly influences population management, health monitoring, and overall production efficiency. Yet, underwater environments often present difficult conditions such as reduced visibility, suspended particles, and uneven lighting that significantly limit the performance of traditional recognition approaches. In this study, we introduce YOLO-CBNet, an enhanced architecture designed to overcome these constraints and provide more dependable monitoring in real-world aquaculture systems. The method adopts a two-stage strategy. First, a Contrast Limited Adaptive Histogram Equalization (CLAHE) module is employed to counter low contrast, turbidity, and color distortion, restoring essential visual information. Second, the YOLOv11 framework is refined through the integration of the Convolutional Block Attention Module (CBAM) and a Bidirectional Feature Pyramid Network (BiFPN), a combination intended to improve multiscale feature fusion and reinforce the detection of small or partially occluded fish. Experimental results indicate that YOLO-CBNet delivers a clear performance boost, the precision score reaches 0.951, surpassing the baseline YOLOv11 model, which achieved 0.911 under the same conditions. These improvements highlight YOLO-CBNet\u2019s potential as a reliable solution for underwater detection and recognition tasks within modern aquaculture environments.<\/jats:p>","DOI":"10.1371\/journal.pone.0341525","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T17:57:43Z","timestamp":1789495063000},"page":"e0341525","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["YOLO-CBNet: A robust attention-enhanced detection framework for underwater fish recognition in aquaculture environments"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7043-8725","authenticated-orcid":true,"given":"Mahdi","family":"Hamzaoui","sequence":"first","affiliation":[{"name":"Innov\u2019COM Laboratory, Higher School of Communication of Tunis, University of Carthage, Technopark Elghazala, Raoued","place":["Ariana, Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leila","family":"Bousbia","sequence":"additional","affiliation":[{"name":"Innov\u2019COM Laboratory, Higher School of Communication of Tunis, University of Carthage, Technopark Elghazala, Raoued","place":["Ariana, Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed","family":"Ould-Elhassen Aoueileyine","sequence":"additional","affiliation":[{"name":"Innov\u2019COM Laboratory, Higher School of Communication of Tunis, University of Carthage, Technopark Elghazala, Raoued","place":["Ariana, Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imen","family":"Filali","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University","place":["Riyadh, Saudi Arabia"],"department":["Department of Computer Sciences"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ridha","family":"Bouallegue","sequence":"additional","affiliation":[{"name":"Innov\u2019COM Laboratory, Higher School of Communication of Tunis, University of Carthage, Technopark Elghazala, Raoued","place":["Ariana, Tunisia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"issue":"66","key":"pone.0341525.ref001","first-page":"7","article-title":"Aquaculture in SOFIA 2022","author":"M Halwart","year":"2022","journal-title":"FAO Aquac Newsl"},{"issue":"10","key":"pone.0341525.ref002","doi-asserted-by":"crossref","first-page":"4898","DOI":"10.3390\/app12104898","article-title":"A Survey of Target Detection and Recognition Methods in Underwater Turbid Areas","volume":"12","author":"X Yuan","year":"2022","journal-title":"Applied Sciences"},{"issue":"4","key":"pone.0341525.ref003","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1111\/raq.12143","article-title":"Application of machine vision systems in aquaculture with emphasis on fish: state\u2010of\u2010the\u2010art and key issues","volume":"9","author":"M Saberioon","year":"2016","journal-title":"Reviews in Aquaculture"},{"key":"pone.0341525.ref004","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.compag.2012.07.008","article-title":"Fish species classification by color, texture and multi-class support vector machine using computer vision","volume":"88","author":"J Hu","year":"2012","journal-title":"Computers and Electronics in Agriculture"},{"issue":"1","key":"pone.0341525.ref005","article-title":"Fish Tracking, Counting, and Behaviour Analysis in Digital Aquaculture: A Comprehensive Survey","volume":"17","author":"M Cui","year":"2025","journal-title":"Reviews in Aquaculture"},{"issue":"3","key":"pone.0341525.ref006","doi-asserted-by":"crossref","first-page":"261","DOI":"10.3390\/agriculture15030261","article-title":"Spray Quality Assessment on Water-Sensitive Paper Comparing AI and Classical Computer Vision Methods","volume":"15","author":"I Sim\u00f5es","year":"2025","journal-title":"Agriculture"},{"issue":"3","key":"pone.0341525.ref007","doi-asserted-by":"crossref","first-page":"125","DOI":"10.3390\/a16030125","article-title":"Assessing the Mass Transfer Coefficient in Jet Bioreactors with Classical Computer Vision Methods and Neural Networks Algorithms","volume":"16","author":"I Nizovtseva","year":"2023","journal-title":"Algorithms"},{"key":"pone.0341525.ref008","article-title":"Deep learning vs. traditional computer vision","volume-title":"Sci Inf Conf","author":"N O\u2019Mahony","year":"2019"},{"issue":"2","key":"pone.0341525.ref009","first-page":"509","article-title":"Comparing classical and deep learning approaches in computer vision","volume":"1","author":"A Olimov","year":"2025","journal-title":"Int J Artif Intell"},{"key":"pone.0341525.ref010","doi-asserted-by":"crossref","unstructured":"Wang W, He B, Zhang L. 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