{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T21:03:33Z","timestamp":1762981413718,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T00:00:00Z","timestamp":1654819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"project KTTSeaDrones","award":["0622_KTTSEADRONES_5_E","POSEUR-03-2215-FC-000046","UIDB\/04326\/2020","UIDP\/04326\/2020","UIDB\/50009\/2020","LA\/P\/0101\/2020","UI\/BD\/151307\/2021"],"award-info":[{"award-number":["0622_KTTSEADRONES_5_E","POSEUR-03-2215-FC-000046","UIDB\/04326\/2020","UIDP\/04326\/2020","UIDB\/50009\/2020","LA\/P\/0101\/2020","UI\/BD\/151307\/2021"]}]},{"name":"PO SEUR program","award":["0622_KTTSEADRONES_5_E","POSEUR-03-2215-FC-000046","UIDB\/04326\/2020","UIDP\/04326\/2020","UIDB\/50009\/2020","LA\/P\/0101\/2020","UI\/BD\/151307\/2021"],"award-info":[{"award-number":["0622_KTTSEADRONES_5_E","POSEUR-03-2215-FC-000046","UIDB\/04326\/2020","UIDP\/04326\/2020","UIDB\/50009\/2020","LA\/P\/0101\/2020","UI\/BD\/151307\/2021"]}]},{"name":"FCT\u2014Foundation for Science and Technology","award":["0622_KTTSEADRONES_5_E","POSEUR-03-2215-FC-000046","UIDB\/04326\/2020","UIDP\/04326\/2020","UIDB\/50009\/2020","LA\/P\/0101\/2020","UI\/BD\/151307\/2021"],"award-info":[{"award-number":["0622_KTTSEADRONES_5_E","POSEUR-03-2215-FC-000046","UIDB\/04326\/2020","UIDP\/04326\/2020","UIDB\/50009\/2020","LA\/P\/0101\/2020","UI\/BD\/151307\/2021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>The first major step in training an object detection model to different classes from the available datasets is the gathering of meaningful and properly annotated data. This recurring task will determine the length of any project, and, more importantly, the quality of the resulting models. This obstacle is amplified when the data available for the new classes are scarce or incompatible, as in the case of fish detection in the open sea. This issue was tackled using a mixed and reversed approach: a network is initiated with a noisy dataset of the same species as our classes (fish), although in different scenarios and conditions (fish from Australian marine fauna), and we gathered the target footage (fish from Portuguese marine fauna; Atlantic Ocean) for the application without annotations. Using the temporal information of the detected objects and augmented techniques during later training, it was possible to generate highly accurate labels from our targeted footage. Furthermore, the data selection method retained the samples of each unique situation, filtering repetitive data, which would bias the training process. The obtained results validate the proposed method of automating the labeling processing, resorting directly to the final application as the source of training data. The presented method achieved a mean average precision of 93.11% on our own data, and 73.61% on unseen data, an increase of 24.65% and 25.53% over the baseline of the noisy dataset, respectively.<\/jats:p>","DOI":"10.3390\/app12125910","type":"journal-article","created":{"date-parts":[[2022,6,10]],"date-time":"2022-06-10T10:25:12Z","timestamp":1654856712000},"page":"5910","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Autonomous Temporal Pseudo-Labeling for Fish Detection"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7557-8304","authenticated-orcid":false,"given":"Ricardo J. M.","family":"Veiga","sequence":"first","affiliation":[{"name":"Institute of Engineering (ISE), University of Algarve, 8005-139 Faro, Portugal"},{"name":"LARSyS, Institute for Systems and Robotics (ISR-Lisbon), 1049-001 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1921-006X","authenticated-orcid":false,"given":"I\u00f1igo E.","family":"Ochoa","sequence":"additional","affiliation":[{"name":"Centre of Marine Sciences (CCMAR), University of Algarve, 8005-139 Faro, Portugal"},{"name":"Marine Biology Research Group, Ghent University, Krijgslaan 281\/S8, B-9000 Ghent, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9718-4250","authenticated-orcid":false,"given":"Adela","family":"Belackova","sequence":"additional","affiliation":[{"name":"Centre of Marine Sciences (CCMAR), University of Algarve, 8005-139 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6884-2886","authenticated-orcid":false,"given":"Lu\u00eds","family":"Bentes","sequence":"additional","affiliation":[{"name":"Centre of Marine Sciences (CCMAR), University of Algarve, 8005-139 Faro, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7046-9646","authenticated-orcid":false,"given":"Jo\u00e3o P.","family":"Silva","sequence":"additional","affiliation":[{"name":"Institute of Engineering (ISE), University of Algarve, 8005-139 Faro, Portugal"},{"name":"LARSyS, Institute for Systems and Robotics (ISR-Lisbon), 1049-001 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7667-7910","authenticated-orcid":false,"given":"Jorge","family":"Semi\u00e3o","sequence":"additional","affiliation":[{"name":"Institute of Engineering (ISE), University of Algarve, 8005-139 Faro, Portugal"},{"name":"INESC-ID, Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1000-029 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3562-6025","authenticated-orcid":false,"given":"Jo\u00e3o M. F.","family":"Rodrigues","sequence":"additional","affiliation":[{"name":"Institute of Engineering (ISE), University of Algarve, 8005-139 Faro, Portugal"},{"name":"LARSyS, Institute for Systems and Robotics (ISR-Lisbon), 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1111\/gcb.13004","article-title":"Global ecological impacts of invasive species in aquatic ecosystems","volume":"22","author":"Gallardo","year":"2016","journal-title":"Glob. Change Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1111\/raq.12464","article-title":"Deep learning for smart fish farming: Applications, opportunities and challenges","volume":"13","author":"Yang","year":"2021","journal-title":"Rev. Aquac."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1111\/2041-210X.13470","article-title":"A field and video annotation guide for baited remote underwater stereo-video surveys of demersal fish assemblages","volume":"11","author":"Langlois","year":"2020","journal-title":"Methods Ecol. Evol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_5","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.fishres.2014.01.019","article-title":"Underwater video techniques for observing coastal marine biodiversity: A review of sixty years of publications (1952\u20132012)","volume":"154","author":"Mallet","year":"2014","journal-title":"Fish. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Moniruzzaman, M., Islam, S.M.S., Bennamoun, M., and Lavery, P. (2017). Deep learning on underwater marine object detection: A survey. Proceedings of the International Conference on Advanced Concepts for Intelligent Vision Systems, Springer.","DOI":"10.1007\/978-3-319-70353-4_13"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liu, S., Li, X., Gao, M., Cai, Y., Nian, R., Li, P., Yan, T., and Lendasse, A. (2018, January 22\u201325). Embedded online fish detection and tracking system via YOLOv3 and parallel correlation filter. Proceedings of the OCEANS 2018 MTS\/IEEE Charleston, Charleston, SC, USA.","DOI":"10.1109\/OCEANS.2018.8604658"},{"key":"ref_9","unstructured":"Stavelin, H., Rasheed, A., San, O., and Hestnes, A.J. (2020). Marine life through You Only Look Once\u2019s perspective. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"De Bie, T., De Raedt, L., Hern\u00e1ndez-Orallo, J., Hoos, H.H., Smyth, P., and Williams, C.K. (2021). Automating Data Science: Prospects and Challenges. arXiv.","DOI":"10.1145\/3495256"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"6988","DOI":"10.1007\/s10489-020-02154-9","article-title":"Temperate fish detection and classification: A deep learning based approach","volume":"52","author":"Wiklund","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_12","unstructured":"Australian Institute of Marine Science (AIMS) (2020, December 08). OzFish Dataset\u2014Machine Learning Dataset for Baited Remote Underwater Video Stations. Available online: https:\/\/doi.org\/10.25845\/5e28f062c5097."},{"key":"ref_13","unstructured":"Yosinski, J., Clune, J., Bengio, Y., and Lipson, H. (2014). How transferable are features in deep neural networks?. arXiv."},{"key":"ref_14","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.Y.M. (2021). You Only Learn One Representation: Unified Network for Multiple Tasks. arXiv."},{"key":"ref_15","unstructured":"O\u2019Mahony, N., Campbell, S., Carvalho, A., Harapanahalli, S., Hernandez, G.V., Krpalkova, L., Riordan, D., and Walsh, J. Deep learning vs. traditional computer vision. Proceedings of the Science and Information Conference."},{"key":"ref_16","first-page":"1625","article-title":"A survey on fish classification techniques","volume":"34","author":"Alsmadi","year":"2022","journal-title":"J. King Saud-Univ.-Comput. Inf. Sci."},{"key":"ref_17","unstructured":"Zou, Z., Shi, Z., Guo, Y., and Ye, J. (2019). Object detection in 20 years: A survey. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3389\/fmars.2020.00429","article-title":"Automating the analysis of fish abundance using object detection: Optimizing animal ecology with deep learning","volume":"7","author":"Ditria","year":"2020","journal-title":"Front. Mar. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, M., Xu, S., Song, W., He, Q., and Wei, Q. (2021). Lightweight Underwater Object Detection Based on YOLO v4 and Multi-Scale Attentional Feature Fusion. Remote Sens., 13.","DOI":"10.3390\/rs13224706"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3388","DOI":"10.1109\/TPAMI.2020.2981890","article-title":"Imbalance problems in object detection: A review","volume":"43","author":"Oksuz","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., and Aroyo, L.M. (2021, January 8\u201313). \u201cEveryone wants to do the model work, not the data work\u201d: Data Cascades in High-Stakes AI. Proceedings of the Conference on Human Factors in Computing Systems, Yokohama Japan.","DOI":"10.1145\/3411764.3445518"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fisher, R.B., Chen-Burger, Y.H., Giordano, D., Hardman, L., and Lin, F.P. (2016). Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data, Springer.","DOI":"10.1007\/978-3-319-30208-9"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Joly, A., Go\u00ebau, H., Glotin, H., Spampinato, C., Bonnet, P., Vellinga, W.P., Planqu\u00e9, R., Rauber, A., Palazzo, S., and Fisher, B. (2015). LifeCLEF 2015: Multimedia life species identification challenges. Proceedings of the International Conference of the Cross-Language Evaluation Forum for European Languages, Springer.","DOI":"10.1007\/978-3-319-24027-5_46"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhuang, P., Wang, Y., and Qiao, Y. (2018, January 22\u201326). Wildfish: A large benchmark for fish recognition in the wild. Proceedings of the 26th ACM international Conference on Multimedia, Seoul, Korea.","DOI":"10.1145\/3240508.3240616"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3603","DOI":"10.1109\/TMM.2020.3028482","article-title":"WildFish++: A Comprehensive Fish Benchmark for Multimedia Research","volume":"23","author":"Zhuang","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-020-71639-x","article-title":"A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis","volume":"10","author":"Saleh","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_27","unstructured":"Islam, M.J., Edge, C., Xiao, Y., Luo, P., Mehtaz, M., Morse, C., Enan, S.S., and Sattar, J. (January, January 24). Semantic segmentation of underwater imagery: Dataset and benchmark. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4173\/mic.2021.1.1","article-title":"NorFisk: Fish image dataset from Norwegian fish farms for species recognition using deep neural networks","volume":"42","author":"Crescitelli","year":"2021","journal-title":"Model. Identif. Control."},{"key":"ref_29","first-page":"1137","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial pyramid pooling in deep convolutional networks for visual recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_34","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_36","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Cutter, G., Stierhoff, K., and Zeng, J. (2015, January 6\u20139). Automated detection of rockfish in unconstrained underwater videos using haar cascades and a new image dataset: Labeled fishes in the wild. Proceedings of the IEEE Winter Applications and Computer Vision Workshops, Waikoloa, HI, USA.","DOI":"10.1109\/WACVW.2015.11"},{"key":"ref_38","unstructured":"Viola, P., and Jones, M. (2001, January 8\u201314). Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), Kauai, Hawaii."},{"key":"ref_39","unstructured":"Choi, S. (2015, January 8\u201311). Fish Identification in Underwater Video with Deep Convolutional Neural Network: SNUMedinfo at LifeCLEF Fish task 2015. Proceedings of the CLEF (Working Notes), Toulouse, France."},{"key":"ref_40","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201326). Histograms of oriented gradients for human detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Their Appl."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_43","unstructured":"Li, X., Shang, M., Qin, H., and Chen, L. (2015, January 19\u201322). Fast accurate fish detection and recognition of underwater images with fast r-cnn. Proceedings of the OCEANS 2015-MTS\/IEEE Washington, Washington, DC, USA."},{"key":"ref_44","unstructured":"Ash, J.T., Zhang, C., Krishnamurthy, A., Langford, J., and Agarwal, A. (2019). Deep batch active learning by diverse, uncertain gradient lower bounds. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.neucom.2015.10.122","article-title":"DeepFish: Accurate underwater live fish recognition with a deep architecture","volume":"187","author":"Qin","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Mandal, R., Connolly, R.M., Schlacher, T.A., and Stantic, B. (2018, January 8\u201313). Assessing fish abundance from underwater video using deep neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489482"},{"key":"ref_47","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_48","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Xu, W., and Matzner, S. (2018, January 12\u201314). Underwater fish detection using deep learning for water power applications. Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA.","DOI":"10.1109\/CSCI46756.2018.00067"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1093\/icesjms\/fsz025","article-title":"Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system","volume":"77","author":"Salman","year":"2020","journal-title":"ICES J. Mar. Sci."},{"key":"ref_51","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":"57","author":"Jalal","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_52","unstructured":"Pedersen, M., Bruslund Haurum, J., Gade, R., and Moeslund, T.B. (2019, January 16\u201317). Detection of marine animals in a new underwater dataset with varying visibility. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA."},{"key":"ref_53","unstructured":"Jiang, Z., Zhao, L., Li, S., and Jia, Y. (2020). Real-time object detection method based on improved YOLOv4-tiny. arXiv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ecoinf.2019.05.004","article-title":"Cascaded deep network systems with linked ensemble components for underwater fish detection in the wild","volume":"52","author":"Labao","year":"2019","journal-title":"Ecol. Inform."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/s10661-020-08653-z","article-title":"Deep learning for automated analysis of fish abundance: The benefits of training across multiple habitats","volume":"192","author":"Ditria","year":"2020","journal-title":"Environ. Monit. Assess."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1080\/23308249.2020.1777083","article-title":"Marine recreational fishing in Portugal: Current knowledge, challenges, and future perspectives","volume":"28","author":"Diogo","year":"2020","journal-title":"Rev. Fish. Sci. Aquac."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_61","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s11263-019-01247-4","article-title":"Deep learning for generic object detection: A survey","volume":"128","author":"Liu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_63","first-page":"1729","article-title":"Train longer, generalize better: Closing the generalization gap in large batch training of neural networks","volume":"30","author":"Hoffer","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/12\/5910\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:27:36Z","timestamp":1760138856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/12\/12\/5910"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,10]]},"references-count":63,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["app12125910"],"URL":"https:\/\/doi.org\/10.3390\/app12125910","relation":{},"ISSN":["2076-3417"],"issn-type":[{"type":"electronic","value":"2076-3417"}],"subject":[],"published":{"date-parts":[[2022,6,10]]}}}