{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:25:55Z","timestamp":1760145955940,"version":"build-2065373602"},"reference-count":68,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T00:00:00Z","timestamp":1727136000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bryden Centre","award":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"],"award-info":[{"award-number":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"]}]},{"name":"European Union\u2019s INTERREG VA Programme","award":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"],"award-info":[{"award-number":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"]}]},{"name":"Special EU Programmes Body (SEUPB)","award":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"],"award-info":[{"award-number":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"]}]},{"DOI":"10.13039\/501100000288","name":"Royal Society Research Grant","doi-asserted-by":"publisher","award":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"],"award-info":[{"award-number":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"]}],"id":[{"id":"10.13039\/501100000288","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000270","name":"NERC VertIBase project","doi-asserted-by":"publisher","award":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"],"award-info":[{"award-number":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"]}],"id":[{"id":"10.13039\/501100000270","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UK Department for Business, Energy, and Industrial Strategy\u2019s offshore energy Strategic Environmental Assessment programme","award":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"],"award-info":[{"award-number":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"]}]},{"DOI":"10.13039\/501100000266","name":"EPSRC Supergen ORE Hub","doi-asserted-by":"publisher","award":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"],"award-info":[{"award-number":["RSG\\R1\\180430","NE\/N01765X\/1","EP\/S000747\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Tidal stream environments are important areas of marine habitat for the development of marine renewable energy (MRE) sources and as foraging hotspots for megafaunal species (seabirds and marine mammals). Hydrodynamic features can promote prey availability and foraging efficiency that influences megafaunal foraging success and behaviour, with the potential for animal interactions with MRE devices. Uncrewed aerial vehicles (UAVs) offer a novel tool for the fine-scale data collection of surface turbulence features and animals, which is not possible through other techniques, to provide information on the potential environmental impacts of anthropogenic developments. However, large imagery datasets are time-consuming to manually review and analyse. This study demonstrates an experimental methodology for the automated detection of turbulence features within UAV imagery. A deep learning architecture, specifically a Faster R-CNN model, was used to autonomously detect kolk-boils within UAV imagery of a tidal stream environment. The model was trained on pre-existing, labelled images of kolk-boils that were pre-treated using a suite of image enhancement techniques based on the environmental conditions present within each image. A 75-epoch model variant provided the highest average recall and precision values; however, it appeared to be limited by sub-optimal detections of false positive values. Although further development is required, including the creation of standardised image data pools, increased model benchmarking and the advancement of tailored pre-processing techniques, this work demonstrates the viability of utilising deep learning to automate the detection of surface turbulence features within a tidal stream environment.<\/jats:p>","DOI":"10.3390\/s24196170","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T08:56:06Z","timestamp":1727168166000},"page":"6170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Experimental Methodology for Automated Detection of Surface Turbulence Features in Tidal Stream Environments"],"prefix":"10.3390","volume":"24","author":[{"given":"James","family":"Slingsby","sequence":"first","affiliation":[{"name":"Environmental Research Institute, University of the Highlands and Islands, Thurso KW14 7EE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Beth E.","family":"Scott","sequence":"additional","affiliation":[{"name":"School of Biological Sciences, University of Aberdeen, Aberdeen AB24 2TZ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7217-0146","authenticated-orcid":false,"given":"Louise","family":"Kregting","sequence":"additional","affiliation":[{"name":"The New Zealand Institute for Plant & Food Research Ltd., 297 Akersten Street, Port Nelson, Nelson 7010, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jason","family":"McIlvenny","sequence":"additional","affiliation":[{"name":"Environmental Research Institute, University of the Highlands and Islands, Thurso KW14 7EE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jared","family":"Wilson","sequence":"additional","affiliation":[{"name":"Marine Scotland Science, Marine Scotland, Edinburgh EH6 6QQ, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanny","family":"Helleux","sequence":"additional","affiliation":[{"name":"Environmental Research Institute, University of the Highlands and Islands, Thurso KW14 7EE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7107-7713","authenticated-orcid":false,"given":"Benjamin J.","family":"Williamson","sequence":"additional","affiliation":[{"name":"Environmental Research Institute, University of the Highlands and Islands, Thurso KW14 7EE, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"275","DOI":"10.3354\/meps11677","article-title":"Riding the Tide: Use of a Moving Tidal-Stream Habitat by Harbour Porpoises","volume":"549","author":"Benjamins","year":"2016","journal-title":"Mar. Ecol. Prog. Ser."},{"key":"ref_2","first-page":"1","article-title":"Confusion Reigns? A Review of Marine Megafauna Interactions with Tidal-Stream Environments","volume":"53","author":"Benjamins","year":"2015","journal-title":"Oceanogr. Mar. Biol. An. Annu. Rev."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lieber, L., Nimmo-Smith, W.A.M., Waggitt, J.J., and Kregting, L. (2019). Localised Anthropogenic Wake Generates a Predictable Foraging Hotspot for Top Predators. Commun. Biol., 2.","DOI":"10.1038\/s42003-019-0364-z"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1093\/icesjms\/fsab017","article-title":"Application of a Multibeam Echosounder to Document Changes in Animal Movement and Behaviour around a Tidal Turbine Structure","volume":"78","author":"Williamson","year":"2021","journal-title":"ICES J. Mar. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/S1471-0846(04)00111-8","article-title":"Windmills below the Sea","volume":"5","author":"Fraenkel","year":"2004","journal-title":"Refocus"},{"key":"ref_6","first-page":"20210469","article-title":"A Review of the UK and British Channel Islands Practical Tidal Stream Energy Resource","volume":"477","author":"Coles","year":"2021","journal-title":"Proc. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.eiar.2011.06.002","article-title":"The Environmental Interactions of Tidal and Wave Energy Generation Devices","volume":"32","author":"Frid","year":"2012","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/j.renene.2011.07.003","article-title":"Impact of Tidal Energy Converter (TEC) Arrays on the Dynamics of Headland Sand Banks","volume":"37","author":"Neill","year":"2012","journal-title":"Renew. Energy"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.apenergy.2013.11.052","article-title":"Tidal Stream Energy Impact on the Transient and Residual Flow in an Estuary: A 3D Analysis","volume":"116","author":"Carballo","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_10","unstructured":"(2022, April 27). Marine Scotland Directorate Science and Research\u2014Marine Renewable Energy, Available online: https:\/\/www.gov.scot\/policies\/marine-renewable-energy\/science-and-research\/."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"111314","DOI":"10.1016\/j.marpolbul.2020.111314","article-title":"Assessing the Effects of Tidal Stream Marine Renewable Energy on Seabirds: A Conceptual Framework","volume":"157","author":"Isaksson","year":"2020","journal-title":"Mar. Pollut. Bull."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1890\/120150","article-title":"Lightweight Unmanned Aerial Vehicles Will Revolutionize Spatial Ecology","volume":"11","author":"Anderson","year":"2013","journal-title":"Front. Ecol. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1093\/icesjms\/fsx100","article-title":"The Potential for Unmanned Aerial Vehicles (UAVs) to Conduct Marine Fauna Surveys in Place of Manned Aircraft","volume":"75","author":"Colefax","year":"2018","journal-title":"ICES J. Mar. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1111\/jofo.12171","article-title":"Computer-Automated Bird Detection and Counts in High-Resolution Aerial Images: A Review","volume":"87","author":"Chabot","year":"2016","journal-title":"J. Field Ornithol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/1365-2656.12780","article-title":"A Computer Vision for Animal Ecology","volume":"87","author":"Weinstein","year":"2018","journal-title":"J. Anim. Ecol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"Lecun","year":"2015","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"L\u00fcrig, M.D., Donoughe, S., Svensson, E.I., Porto, A., and Tsuboi, M. (2021). Computer Vision, Machine Learning, and the Promise of Phenomics in Ecology and Evolutionary Biology. Front. Ecol. Evol., 9.","DOI":"10.3389\/fevo.2021.642774"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shalev-Shwartz, S., and Ben-David, S. (2013). Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press. [1st ed.].","DOI":"10.1017\/CBO9781107298019"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.3897\/oneeco.1.e8621","article-title":"Adoption of Machine Learning Techniques in Ecology and Earth Science","volume":"1","author":"Thessen","year":"2016","journal-title":"One Ecosyst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1086\/587826","article-title":"Machine Learning Methods without Tears: A Primer for Ecologists Author","volume":"83","author":"Olden","year":"2008","journal-title":"Q. Rev. Biol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2783","DOI":"10.1890\/07-0539.1","article-title":"Random Forests for Classification in Ecology","volume":"88","author":"Cutler","year":"2007","journal-title":"Ecology"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1561\/2000000039","article-title":"Deep Learning: Methods and Applications","volume":"7","author":"Deng","year":"2013","journal-title":"Found. Trends Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1111\/2041-210X.13133","article-title":"Past, Present and Future Approaches Using Computer Vision for Animal Re-Identification from Camera Trap Data","volume":"10","author":"Schneider","year":"2019","journal-title":"Methods Ecol. Evol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Schneider, S., Taylor, G.W., and Kremer, S. (2018, January 8-10). Deep Learning Object Detection Methods for Ecological Camera Trap Data. Proceedings of the 2018 15th Conference on Computer and Robot Vision, CRV 2018, Toronto, ON, Canada.","DOI":"10.1109\/CRV.2018.00052"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Jiang, H., and Learned-Miller, E. (June, January 30). Face Detection with the Faster R-CNN. Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2017\u20141st International Workshop on Adaptive Shot Learning for Gesture Understanding and Production, ASL4GUP 2017, Biometrics in the Wild, Bwild 2017, Heteroge, Washington, DC, USA.","DOI":"10.1109\/FG.2017.82"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Albawi, S., Mohammed, T.A.M., and Alzawi, S. (2017, January 21-23). Layers of a Convolutional Neural Network. Proceedings of the ICET2017, Antalya, Turkey.","DOI":"10.1109\/ICEngTechnol.2017.8308186"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1175\/JTECH-D-11-00223.1","article-title":"Current Patterns in the Inner Sound (Pentland Firth) from Underway ADCP Data","volume":"30","author":"Woolf","year":"2013","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"McIlvenny, J., Tamsett, D., Gillibrand, P., and Goddijn-Murphy, L. (2016). On the Sediment Dynamics in a Tidally Energetic Channel: The Inner Sound, Northern Scotland. J. Mar. Sci. Eng., 4.","DOI":"10.3390\/jmse4020031"},{"key":"ref_29","unstructured":"(2020, July 17). SIMEC MeyGen | Tidal Projects | SIMEC Atlantis Energy. Available online: https:\/\/saerenewables.com\/tidal-stream\/meygen\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.marenvres.2018.01.007","article-title":"Black Guillemot Ecology in Relation to Tidal Stream Energy Generation: An Evaluation of Current Knowledge and Information Gaps","volume":"134","author":"Johnston","year":"2018","journal-title":"Mar. Environ. Res."},{"key":"ref_31","unstructured":"(2020, July 17). DJI Phantom 4 Pro\u2014Specs, Tutorials & Guides\u2014DJI. Available online: https:\/\/www.dji.com\/uk\/phantom-4-pro\/info#specs."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"R404","DOI":"10.1016\/j.cub.2016.04.001","article-title":"Best Practice for Minimising Unmanned Aerial Vehicle Disturbance to Wildlife in Biological Field Research","volume":"26","author":"Hodgson","year":"2016","journal-title":"Current Biology"},{"key":"ref_33","unstructured":"(2024, May 26). Civil Aviation Authority UK CAA Drone Code, Available online: https:\/\/www.bedford.gov.uk\/files\/drones-model-aircraft-policy.pdf\/download?inline."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Slingsby, J., Scott, B.E., Kregting, L., McIlvenny, J., Wilson, J., Couto, A., Roos, D., Yanez, M., and Williamson, B.J. (2021). Surface Characterisation of Kolk-Boils within Tidal Stream Environments Using UAV Imagery. J. Mar. Sci. Eng., 9.","DOI":"10.3389\/fmars.2022.820722"},{"key":"ref_35","unstructured":"(2022, March 09). PIX4D PIX4Dcapture: Free Drone Flight Planning Mobile App | Pix4D. Available online: https:\/\/www.pix4d.com\/product\/pix4dcapture."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"11","DOI":"10.36688\/imej.6.11-17","article-title":"The Bigger Picture: Developing a Low-Cost Graphical User Interface to Process Drone Imagery of Tidal Stream Environments","volume":"6","author":"Slingsby","year":"2023","journal-title":"Int. Mar. Energy J."},{"key":"ref_37","unstructured":"(2023, May 29). GitHub Microsoft\/VoTT: Visual Object Tagging Tool: An Electron App for Building End to End Object Detection Models from Images and Videos. Available online: https:\/\/github.com\/microsoft\/VoTT."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","unstructured":"Van Rossum, G., and Drake, F.L. (2009). Python 3 Reference Manual, CreateSpace."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Chaudhary, A., Chouhan, K.S., Gajrani, J., and Sharma, B. (2020). Deep Learning with PyTorch, Manning Publications Co.","DOI":"10.4018\/978-1-7998-3095-5.ch003"},{"key":"ref_41","unstructured":"(2022, March 04). NVIDIA CUDA Toolkit Documentation. Available online: https:\/\/developer.nvidia.com\/cuda-toolkit."},{"key":"ref_42","unstructured":"(2022, March 11). NVIDIA GeForce RTX 30 Series Graphics Card Overview. Available online: https:\/\/www.nvidia.com\/en-gb\/geforce\/graphics-cards\/30-series\/."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.rse.2018.06.028","article-title":"Detecting Mammals in UAV Images: Best Practices to Address a Substantially Imbalanced Dataset with Deep Learning","volume":"216","author":"Kellenberger","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_44","unstructured":"Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., and Ren, D. (2020, January 7\u201312). Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression. Proceedings of the AAAI 2020\u201434th AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Moniruzzaman, M., Islam, S.M.S., Lavery, P., and Bennamoun, M. (2019, January 2\u20134). Faster R-CNN Based Deep Learning for Seagrass Detection from Underwater Digital Images. Proceedings of the 2019 Digital Image Computing: Techniques and Applications, DICTA 2019, Perth, Australia.","DOI":"10.1109\/DICTA47822.2019.8946048"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kandimalla, V., Richard, M., Smith, F., Quirion, J., Torgo, L., and Whidden, C. (2022). Automated Detection, Classification and Counting of Fish in Fish Passages with Deep Learning. Front. Mar. Sci., 8.","DOI":"10.3389\/fmars.2021.823173"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1007\/s11517-019-02099-3","article-title":"Localization of Common Carotid Artery Transverse Section in B-Mode Ultrasound Images Using Faster RCNN: A Deep Learning Approach","volume":"58","author":"Jain","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Raynal, A.M., and Doerry, A.W. (2010). Doppler Characteristics of Sea Clutter.","DOI":"10.2172\/992329"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1002\/rse2.205","article-title":"Machine Learning to Detect Marine Animals in UAV Imagery: Effect of Morphology, Spacing, Behaviour and Habitat","volume":"7","author":"Dujon","year":"2021","journal-title":"Remote Sens. Ecol. Conserv."},{"key":"ref_50","unstructured":"Slingsby, J., Scott, B., Kregting, L., Mcilvenny, J., Wilson, J., Yanez, M., and Williamson, B.J. (2021, January 5\u20139). The Bigger Picture: Developing a Graphical User Interface to Process UAV Imagery of Tidal Stream Environments. Proceedings of the EWTEC Proceedings, Virtual, Online."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hong, S.J., Han, Y., Kim, S.Y., Lee, A.Y., and Kim, G. (2019). Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery. Sensors, 19.","DOI":"10.3390\/s19071651"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"8509","DOI":"10.1007\/s13369-021-05471-4","article-title":"An Improved Faster-RCNN Model for Handwritten Character Recognition","volume":"46","author":"Albahli","year":"2021","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1093\/icesjms\/fsab255","article-title":"Unlocking the Potential of Deep Learning for Marine Ecology: Overview, Applications, and Outlook","volume":"79","author":"Goodwin","year":"2022","journal-title":"ICES J. Mar. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Borowicz, A., Le, H., Humphries, G., Nehls, G., H\u00f6schle, C., Kosarev, V., and Lynch, H.J. (2019). Aerial-Trained Deep Learning Networks for Surveying Cetaceans from Satellite Imagery. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0212532"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1111\/2041-210X.12973","article-title":"How Do You Find the Green Sheep? A Critical Review of the Use of Remotely Sensed Imagery to Detect and Count Animals","volume":"9","author":"Hollings","year":"2018","journal-title":"Methods Ecol. Evol."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Reza, A.M. (2004). Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement, Springer.","DOI":"10.1023\/B:VLSI.0000028532.53893.82"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"26545","DOI":"10.1007\/s11042-018-5878-8","article-title":"Underwater Image Enhancement Using Blending of CLAHE and Percentile Methodologies","volume":"77","author":"Garg","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_58","first-page":"293","article-title":"New Workflow for Marine Fish Classification Based on Combination Features and CLAHE Enhancement Technique","volume":"13","author":"Pramunendar","year":"2020","journal-title":"Int. J. Intell. Eng. Syst."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e02163","DOI":"10.1002\/ecs2.2163","article-title":"Futurecasting Ecological Research: The Rise of Technoecology","volume":"9","author":"Allan","year":"2018","journal-title":"Ecosphere"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"114995","DOI":"10.1016\/j.oceaneng.2023.114995","article-title":"A Large-Scale Review of Wave and Tidal Energy Research over the Last 20 Years","volume":"282","author":"Khojasteh","year":"2023","journal-title":"Ocean. Eng."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1829","DOI":"10.1093\/icesjms\/fsad100","article-title":"Machine Learning in Marine Ecology: An Overview of Techniques and Applications","volume":"80","author":"Rubbens","year":"2023","journal-title":"ICES J. Mar. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ditria, E.M., Lopez-Marcano, S., Sievers, M., Jinks, E.L., Brown, C.J., and Connolly, R.M. (2020). Automating the Analysis of Fish Abundance Using Object Detection: Optimizing Animal Ecology with Deep Learning. Front. Mar. Sci., 7.","DOI":"10.3389\/fmars.2020.00429"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Loussaief, S., and Abdelkrim, A. (2018, January 22\u201325). Deep Learning vs. Bag of Features in Machine Learning for Image Classification. Proceedings of the 2018 International Conference on Advanced Systems and Electric Technologies, IC_ASET 2018, Hammamet, Tunisia.","DOI":"10.1109\/ASET.2018.8379825"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e4080","DOI":"10.1002\/ecs2.4080","article-title":"Tidal Streams, Fish, and Seabirds: Understanding the Linkages between Mobile Predators, Prey, and Hydrodynamics","volume":"13","author":"Couto","year":"2022","journal-title":"Ecosphere"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"107474","DOI":"10.1016\/j.ecss.2021.107474","article-title":"Modelling Hydrodynamics of Fast Tidal Stream around a Promontory Headland","volume":"259","author":"McIlvenny","year":"2021","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_66","unstructured":"Boulais, O., Woodward, B., Schlining, B., Lundsten, L., Barnard, K., Bell, K.C., and Katija, K. (2020). FathomNet: An Underwater Image Training Database for Ocean Exploration and Discovery. arXiv."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Langenk\u00e4mper, D., van Kevelaer, R., Purser, A., and Nattkemper, T.W. (2020). Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Front. Mar. Sci., 7.","DOI":"10.3389\/fmars.2020.00506"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1632","DOI":"10.1111\/2041-210X.13256","article-title":"Applications for Deep Learning in Ecology","volume":"10","author":"Christin","year":"2019","journal-title":"Methods Ecol. Evol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6170\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:01:34Z","timestamp":1760112094000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/19\/6170"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,9,24]]},"references-count":68,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["s24196170"],"URL":"https:\/\/doi.org\/10.3390\/s24196170","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,9,24]]}}}