Academic Journal
AquaVision : AI-powered marine species identification
| Τίτλος: | AquaVision : AI-powered marine species identification |
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| Συγγραφείς: | Mifsud Scicluna, Benjamin, Gauci, Adam, Deidun, Alan |
| Στοιχεία εκδότη: | MDPI |
| Έτος έκδοσης: | 2024 |
| Συλλογή: | University of Malta: OAR@UM / L-Università ta' Malta |
| Θεματικοί όροι: | Image analysis, Machine learning, Neural networks (Computer science), Science -- Social aspects, Introduced organisms |
| Περιγραφή: | This study addresses the challenge of accurately identifying fish species by using machine learning and image classification techniques. The primary aim is to develop an innovative algorithm that can dynamically identify the most common (within Maltese coastal waters) invasive Mediterranean fish species based on available images. In particular, these include Fistularia commersonii, Lobotes surinamensis, Pomadasys incisus, Siganus luridus, and Stephanolepis diaspros, which have been adopted as this study’s target species. Through the use of machine-learning models and transfer learning, the proposed solution seeks to enable precise, on-the-spot species recognition. The methodology involved collecting and organising images as well as training the models with consistent datasets to ensure comparable results. After trying a number of models, ResNet18 was found to be the most accurate and reliable, with YOLO v8 following closely behind. While the performance of YOLO was reasonably good, it exhibited less consistency in its results. These results underline the potential of the developed algorithm to significantly aid marine biology research, including citizen science initiatives, and promote environmental management efforts through accurate fish species identification. ; peer-reviewed |
| Τύπος εγγράφου: | article in journal/newspaper |
| Γλώσσα: | English |
| Relation: | https://www.um.edu.mt/library/oar/handle/123456789/124985 |
| DOI: | 10.3390/info15080437 |
| Διαθεσιμότητα: | https://www.um.edu.mt/library/oar/handle/123456789/124985 https://doi.org/10.3390/info15080437 |
| Rights: | info:eu-repo/semantics/openAccess ; The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder |
| Αριθμός Καταχώρησης: | edsbas.7B63CB6F |
| Βάση Δεδομένων: | BASE |
| DOI: | 10.3390/info15080437 |
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