Academic Journal
Image-based, unsupervised estimation of fish size from commercial landings using deep learning
| العنوان: | Image-based, unsupervised estimation of fish size from commercial landings using deep learning |
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| المؤلفون: | Álvarez-Ellacuria, Amaya, Palmer, Miquel, Catalán, Ignacio Alberto, Lisani, José Luis |
| المساهمون: | Fundación Biodiversidad, Govern de les Illes Balears, European Commission, Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España) |
| بيانات النشر: | Oxford University Press |
| سنة النشر: | 2020 |
| المجموعة: | Digital.CSIC (Consejo Superior de Investigaciones Científicas / Spanish National Research Council) |
| مصطلحات موضوعية: | Convolutional neural networks, Deep learning, Fish size estimation, Landings |
| الوصف: | The dynamics of fish length distribution is a key input for understanding the fish population dynamics and taking informed management decisions on exploited stocks. Nevertheless, in most fisheries, the length of landed fish is still made by hand. As a result, length estimation is precise at fish level, but due to the inherent high costs of manual sampling, the sample size tends to be small. Accordingly, the precision of population-level estimates is often suboptimal and prone to bias when properly stratified sampling programmes are not affordable. Recent applications of artificial intelligence to fisheries science are opening a promising opportunity for the massive sampling of fish catches. Here, we present the results obtained using a deep convolutional network (Mask R-CNN) for unsupervised (i.e. fully automatic) European hake length estimation from images of fish boxes automatically collected at the auction centre. The estimated mean of fish lengths at the box level is accurate; for average lengths ranging 20–40 cm, the root-mean-square deviation was 1.9 cm, and maximum deviation between the estimated and the measured mean body length was 4.0 cm. We discuss the challenges and opportunities that arise with the use of this technology to improve data acquisition in fisheries. ; This work has been funded by the projects FOTOPEIX and FOTOPEX2 (2017/2279 and 2018/2002) from Fundación Biodiversidad, through the Pleamar Program. We specially thank OPMALLORCAMAR and Direcció General de Pesca del Govern de les Illes Balears for supporting these projects. The work of J-LL was partially supported by grants TIN2017-85572-P and DPI2017-86372-C3-3-R (MINECO/AEI/FEDERUE). |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| تدمد: | 1054-3139 |
| Relation: | #PLACEHOLDER_PARENT_METADATA_VALUE#; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/TIN2017-85572-P; info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/DPI2017-86372-C3-3-R; Postprint; http://doi.org/10.1093/icesjms/fsz216; Sí; e-issn: 1095-9289; ICES Journal of Marine Science 77(4): 1330-1339 (2020); http://hdl.handle.net/10261/226250; http://dx.doi.org/10.13039/501100011033; http://dx.doi.org/10.13039/501100000780 |
| DOI: | 10.1093/icesjms/fsz216 |
| DOI: | 10.13039/501100011033 |
| DOI: | 10.13039/501100000780 |
| الاتاحة: | http://hdl.handle.net/10261/226250 https://doi.org/10.1093/icesjms/fsz216 https://doi.org/10.13039/501100011033 https://doi.org/10.13039/501100000780 |
| Rights: | open |
| رقم الانضمام: | edsbas.BFE83142 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 10543139 |
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| DOI: | 10.1093/icesjms/fsz216 |