{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:50:46Z","timestamp":1792785046269,"version":"4.5.0"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T00:00:00Z","timestamp":1782259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":85,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University","award":["PNURSP2026R904"],"award-info":[{"award-number":["PNURSP2026R904"]}],"id":[{"id":"https:\/\/ror.org\/05b0cyh02","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-59151-0","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T23:26:16Z","timestamp":1782257176000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BiOLO-Wave: a bio-inspired chromatic-deformable attention network for camouflaged fish detection in turbid underwater environments"],"prefix":"10.1038","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7043-8725","authenticated-orcid":false,"given":"Mahdi","family":"Hamzaoui","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamed Ould-Elhassen","family":"Aoueileyine","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Imen","family":"Filali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ridha","family":"Bouallegue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,24]]},"reference":[{"key":"59151_CR1","volume-title":"Data for Background Report 2024\u2013FAO State of Food and Agriculture","author":"S Lord","year":"2024","unstructured":"Lord, S. Data for Background Report 2024\u2013FAO State of Food and Agriculture (Food and Agriculture Organization of the United Nations (FAO), Rome, 2024)."},{"issue":"1","key":"59151_CR2","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1111\/raq.12556","volume":"14","author":"MO Barreto","year":"2022","unstructured":"Barreto, M. O. et al. Emerging indicators of fish welfare in aquaculture. Rev. Aquac. 14(1), 343\u2013361. https:\/\/doi.org\/10.1111\/raq.12556 (2022).","journal-title":"Rev. Aquac."},{"issue":"1","key":"59151_CR3","doi-asserted-by":"publisher","DOI":"10.1111\/raq.13001","volume":"17","author":"M Cui","year":"2025","unstructured":"Cui, M. et al. Fish tracking, counting, and behaviour analysis in digital aquaculture: A comprehensive survey. Rev. Aquac. 17(1), e13001. https:\/\/doi.org\/10.1111\/raq.13001 (2025).","journal-title":"Rev. Aquac."},{"issue":"4","key":"59151_CR4","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1111\/raq.12143","volume":"9","author":"M Saberioon","year":"2017","unstructured":"Saberioon, M. et al. Application of machine vision systems in aquaculture with emphasis on fish: state-of-the-art and key issues. Rev. Aquac. 9(4), 369\u2013387. https:\/\/doi.org\/10.1111\/raq.12143 (2017).","journal-title":"Rev. Aquac."},{"key":"59151_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2023.103180","volume":"117","author":"A Eftekhari","year":"2023","unstructured":"Eftekhari, A., Samadzadegan, F. & Dadrass Javan, F. Building change detection using the parallel spatial-channel attention block and edge-guided deep network. Int. J. Appl. Earth Obs. Geoinf. 117, 103180. https:\/\/doi.org\/10.1016\/j.jag.2023.103180 (2023).","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"1","key":"59151_CR6","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1080\/01652176.2022.2057069","volume":"42","author":"M Ziarati","year":"2022","unstructured":"Ziarati, M. et al. Zoonotic diseases of fish and their prevention and control. Vet. Q. 42(1), 95\u2013118. https:\/\/doi.org\/10.1080\/01652176.2022.2057069 (2022).","journal-title":"Vet. Q."},{"issue":"3","key":"59151_CR7","doi-asserted-by":"publisher","first-page":"3725","DOI":"10.1109\/JIOT.2023.329xxxx","volume":"11","author":"Y Li","year":"2023","unstructured":"Li, Y. et al. Underwater visibility enhancement IoT system in extreme environment. IEEE Internet Things J. 11(3), 3725\u20133732. https:\/\/doi.org\/10.1109\/JIOT.2023.329xxxx (2023).","journal-title":"IEEE Internet Things J."},{"key":"59151_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.optlastec.2025.114496","volume":"194","author":"W Zhang","year":"2026","unstructured":"Zhang, W. et al. Turbidity-graded adaptive underwater image enhancement. Opt. Laser Technol. 194, 114496. https:\/\/doi.org\/10.1016\/j.optlastec.2025.114496 (2026).","journal-title":"Opt. Laser Technol."},{"issue":"9","key":"59151_CR9","doi-asserted-by":"publisher","first-page":"2","DOI":"10.4172\/2325-9809.10002","volume":"10","author":"PCS Reddy","year":"2021","unstructured":"Reddy, P. C. S. et al. Image and signal processing in the underwater environment. J. Nucl. Energy Sci. Power Gener. Technol. 10(9), 2. https:\/\/doi.org\/10.4172\/2325-9809.10002 (2021).","journal-title":"J. Nucl. Energy Sci. Power Gener. Technol."},{"issue":"23","key":"59151_CR10","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.3390\/ani12233349","volume":"12","author":"F Song","year":"2022","unstructured":"Song, F. et al. The effect of background color on skin color variation of juvenile Plectropomus leopardus. Animals 12(23), 3349. https:\/\/doi.org\/10.3390\/ani12233349 (2022).","journal-title":"Animals"},{"key":"59151_CR11","doi-asserted-by":"publisher","unstructured":"Scriven, J., Laporte-Weywada, P. & Cruz, J. Introducing non-rigid body structural dynamics to WEC-Sim. Int. Mar. Energy J. 3(2), 55\u201363. https:\/\/doi.org\/10.36688\/imej.3.55-63 (2020).","DOI":"10.36688\/imej.3.55-63"},{"key":"59151_CR12","doi-asserted-by":"publisher","unstructured":"Liu, Y., et al. Advancements in Computer Vision\u2013Based Fish Multi-Object Tracking: A Comprehensive Review. Arch. Comput. Methods Eng. 1\u201328. https:\/\/doi.org\/10.1007\/s11831-025-xxxxx (2026).","DOI":"10.1007\/s11831-025-xxxxx"},{"key":"59151_CR13","doi-asserted-by":"publisher","unstructured":"Lim, L. W. K. Implementation of artificial intelligence in aquaculture and fisheries: deep learning, machine vision, big data, internet of things, robots and beyond. J. Comput. Cogn. Eng. 3(2), 112\u2013118. https:\/\/doi.org\/10.47852\/bonviewJCCE3202124 (2024).","DOI":"10.47852\/bonviewJCCE3202124"},{"issue":"2","key":"59151_CR14","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1177\/1475921719857634","volume":"19","author":"T Guo","year":"2020","unstructured":"Guo, T. et al. Damage detection in a novel deep-learning framework: a robust method for feature extraction. Struct. Health Monit. 19(2), 424\u2013442. https:\/\/doi.org\/10.1177\/1475921719857634 (2020).","journal-title":"Struct. Health Monit."},{"key":"59151_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.107038","volume":"98","author":"C Li","year":"2020","unstructured":"Li, C., Anwar, S. & Porikli, F. Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn. 98, 107038 (2020).","journal-title":"Pattern Recogn."},{"issue":"20","key":"59151_CR16","doi-asserted-by":"publisher","first-page":"26545","DOI":"10.1007\/s11042-018-5878-8","volume":"77","author":"D Garg","year":"2018","unstructured":"Garg, D., Garg, N. K. & Kumar, M. Underwater image enhancement using blending of CLAHE and percentile methodologies. Multimed. Tools Appl. 77(20), 26545\u201326561. https:\/\/doi.org\/10.1007\/s11042-018-5878-8 (2018).","journal-title":"Multimed. Tools Appl."},{"key":"59151_CR17","doi-asserted-by":"publisher","first-page":"6707328","DOI":"10.1155\/2020\/6707328","volume":"2020","author":"F Han","year":"2020","unstructured":"Han, F., Yao, J., Zhu, H. & Wang, C. Underwater Image Processing and Object Detection Based on Deep CNN Method. J. Sens. 2020, 6707328. https:\/\/doi.org\/10.1155\/2020\/6707328 (2020).","journal-title":"J. Sens."},{"key":"59151_CR18","doi-asserted-by":"publisher","first-page":"5981","DOI":"10.3390\/s24185981","volume":"24","author":"X Huang","year":"2024","unstructured":"Huang, X., Liang, C., Li, X. & Kang, F. An underwater crack detection system combining new underwater image-processing technology and an improved YOLOv9 network. Sensors 24, 5981. https:\/\/doi.org\/10.3390\/s24185981 (2024).","journal-title":"Sensors"},{"key":"59151_CR19","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., & Kweon, I. S. CBAM: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV), (2018).","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"59151_CR20","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L. & Sun, G. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018).","DOI":"10.1109\/CVPR.2018.00745"},{"key":"59151_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Q. ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020).","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"59151_CR22","doi-asserted-by":"publisher","DOI":"10.2139\/ssrn.5635890","author":"W Jiao","year":"2025","unstructured":"Jiao, W., Wang, P. & Qi, Y. ECA-UNet: Efficient Channel Attention Enhanced U-Net for Image Segmentation. SSRN Preprint https:\/\/doi.org\/10.2139\/ssrn.5635890 (2025).","journal-title":"SSRN Preprint"},{"key":"59151_CR23","doi-asserted-by":"publisher","DOI":"10.1111\/raq.12985","volume":"17","author":"K Zhang","year":"2025","unstructured":"Zhang, K. et al. Water quality impact on fish behavior: A review from an aquaculture perspective. Rev. Aquac. 17, e12985. https:\/\/doi.org\/10.1111\/raq.12985 (2025).","journal-title":"Rev. Aquac."},{"key":"59151_CR24","doi-asserted-by":"publisher","first-page":"4077","DOI":"10.1007\/s10462-021-10109-3","volume":"55","author":"D Li","year":"2022","unstructured":"Li, D. & Du, L. Recent advances of deep learning algorithms for aquacultural machine vision systems with emphasis on fish. Artif. Intell. Rev. 55, 4077\u20134116. https:\/\/doi.org\/10.1007\/s10462-021-10109-3 (2022).","journal-title":"Artif. Intell. Rev."},{"key":"59151_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3534098","author":"M Elmezain","year":"2025","unstructured":"Elmezain, M. et al. Advancing underwater vision: a survey of deep learning models for underwater object recognition and tracking. IEEE Access https:\/\/doi.org\/10.1109\/ACCESS.2025.3534098 (2025).","journal-title":"IEEE Access"},{"key":"59151_CR26","unstructured":"Hamelijnck, O. Multi-resolution multi-task Gaussian processes. In Advances in Neural Information Processing Systems (NeurIPS), 32 (2019)."},{"issue":"2","key":"59151_CR27","first-page":"31","volume":"1","author":"G Othman","year":"2020","unstructured":"Othman, G. & Zeebaree, D. Q. The applications of discrete wavelet transform in image processing: A review. J. Soft Comput. Data Min. 1(2), 31\u201343 (2020).","journal-title":"J. Soft Comput. Data Min."},{"key":"59151_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2021.3098432","volume":"60","author":"M Zand","year":"2021","unstructured":"Zand, M., Etemad, A. & Greenspan, M. Oriented bounding boxes for small and freely rotated objects. IEEE Trans. Geosci. Remote Sens. 60, 1\u201315. https:\/\/doi.org\/10.1109\/TGRS.2021.3098432 (2021).","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"59151_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-xxxxx-x","author":"C Li","year":"2025","unstructured":"Li, C. Oriented object detection in remote sensing images based on angle quality estimation. Sci. Rep. https:\/\/doi.org\/10.1038\/s41598-025-xxxxx-x (2025).","journal-title":"Sci. Rep."},{"key":"59151_CR30","doi-asserted-by":"crossref","unstructured":"Wang, X. Optimized YOLOv8s framework with deformable convolution for underwater object detection. Sci. Rep. (2025).","DOI":"10.1038\/s41598-025-28989-1"},{"key":"59151_CR31","unstructured":"Hamzaoui, M. et al. YOLOViT Dataset: Underwater Fish Detection Dataset (Version 9). Roboflow Platform, 2024. https:\/\/app.roboflow.com\/aquaculturesupcom\/yolovit-dataset\/9 (accessed 18 Apr 2026)."},{"key":"59151_CR32","first-page":"599","volume":"12","author":"\u017d Vujovi\u0107","year":"2021","unstructured":"Vujovi\u0107, \u017d. Classification model evaluation metrics. Int. J. Adv. Comput. Sci. Appl. 12, 599\u2013606 (2021).","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"59151_CR33","doi-asserted-by":"crossref","unstructured":"Marinescu, R. Detection Strategies: Metrics-Based Rules for Detecting Design Flaws. In Proceedings of the 20th IEEE International Conference on Software Maintenance 350\u2013359 (IEEE, 2004) \u201c\u2018::contentReference[oaicite:0]index=0.","DOI":"10.1109\/ICSM.2004.1357820"},{"key":"59151_CR34","doi-asserted-by":"publisher","unstructured":"Fnu, N., Bansal, A. Understanding the architecture of vision transformer and its variants: A review. In Proceedings of the 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR) (IEEE, 2024). https:\/\/doi.org\/10.1109\/ICIESTR60274.2024.104XXXX","DOI":"10.1109\/ICIESTR60274.2024.104XXXX"},{"key":"59151_CR35","doi-asserted-by":"publisher","unstructured":"Del Prete, R., Graziano, M. D., & Renga, A. RetinaNet: A deep learning architecture to achieve a robust wake detector in SAR images. In Proceedings of the IEEE 6th International Forum on Research and Technology for Society and Industry (RTSI) (IEEE, 2021). https:\/\/doi.org\/10.1109\/RTSI50628.2021.9597320","DOI":"10.1109\/RTSI50628.2021.9597320"},{"issue":"4","key":"59151_CR36","doi-asserted-by":"publisher","first-page":"356","DOI":"10.3390\/aerospace12040356","volume":"12","author":"N Suvittawat","year":"2025","unstructured":"Suvittawat, N. et al. Advances in aircraft skin defect detection using computer vision: A survey and comparison of YOLOv9 and RT-DETR performance. Aerospace 12(4), 356. https:\/\/doi.org\/10.3390\/aerospace12040356 (2025).","journal-title":"Aerospace"},{"key":"59151_CR37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01096","author":"M Tan","year":"2020","unstructured":"Tan, M., Pang, R. & Le, Q. V. EfficientDet: Scalable and efficient object detection. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) https:\/\/doi.org\/10.1109\/CVPR42600.2020.01096 (2020).","journal-title":"In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"59151_CR38","doi-asserted-by":"crossref","unstructured":"J\u00e4ger, J. Croatian Fish Dataset: Fine-Grained Classification of Fish Species in Their Natural Habitat. In Proceedings of the British Machine Vision Conference (BMVC) (Swansea, UK, 2015), 6.1\u20136.12.","DOI":"10.5244\/C.29.MVAB.6"},{"key":"59151_CR39","volume-title":"Fish4Knowledge: collecting and analyzing massive coral reef fish video data","year":"2016","unstructured":"Fisher, R. B. et al. (eds) Fish4Knowledge: collecting and analyzing massive coral reef fish video data Vol. 104 (Springer, Berlin\/Heidelberg, Germany, 2016)."},{"issue":"1","key":"59151_CR40","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1038\/s41597-022-01372-3","volume":"9","author":"N Garcia-d\u2019Urso","year":"2022","unstructured":"Garcia-d\u2019Urso, N. et al. The DeepFish computer vision dataset for fish instance segmentation, classification, and size estimation. Sci. Data 9(1), 287. https:\/\/doi.org\/10.1038\/s41597-022-01372-3 (2022).","journal-title":"Sci. Data"},{"key":"59151_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2022.101847","volume":"72","author":"A Al Muksit","year":"2022","unstructured":"Al Muksit, A. et al. YOLO-Fish: A robust fish detection model to detect fish in realistic underwater environment. Eco. Inform. 72, 101847. https:\/\/doi.org\/10.1016\/j.ecoinf.2022.101847 (2022).","journal-title":"Eco. Inform."},{"issue":"18","key":"59151_CR42","doi-asserted-by":"publisher","first-page":"3780","DOI":"10.3390\/electronics13183780","volume":"13","author":"C Yang","year":"2024","unstructured":"Yang, C., Xiang, J., Li, X. & Xie, Y. FishDet-YOLO: Enhanced underwater fish detection with richer gradient flow and long-range dependency capture through Mamba-C2f. Electronics 13(18), 3780. https:\/\/doi.org\/10.3390\/electronics13183780 (2024).","journal-title":"Electronics"},{"key":"59151_CR43","doi-asserted-by":"crossref","unstructured":"Veiga, R. J. M. & Rodrigues, J. M. F. Fine-Grained Fish Classification from small to large datasets with Vision Transformers. IEEE Access (2024).","DOI":"10.1109\/ACCESS.2024.3443654"},{"issue":"3","key":"59151_CR44","doi-asserted-by":"publisher","first-page":"144","DOI":"10.24843\/LKJITI.2020.v11.i03.p03","volume":"11","author":"Y Adiwinata","year":"2020","unstructured":"Adiwinata, Y. et al. Fish species recognition with faster R-CNN inception-v2 using QUT fish dataset. Lontar Komputer: Jurnal Ilmiah Teknologi Informasi 11(3), 144\u2013154 (2020).","journal-title":"Lontar Komputer: Jurnal Ilmiah Teknologi Informasi"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-59151-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-59151-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-59151-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T22:37:45Z","timestamp":1789598265000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-59151-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,24]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["59151"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-59151-0","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,24]]},"assertion":[{"value":"23 April 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"28942"}}