Deep learning for plankton and coral classification
| Autor: | Alessandra Lumini, Loris Nanni, Gianluca Maguolo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Applied Computing and Informatics, Vol 19, Iss 3/4, Pp 265-283 (2023) |
| Druh dokumentu: | article |
| ISSN: | 2210-8327 2634-1964 |
| DOI: | 10.1016/j.aci.2019.11.004/full/pdf |
| Popis: | In this paper, we present a study about an automated system for monitoring underwater ecosystems. The system here proposed is based on the fusion of different deep learning methods. We study how to create an ensemble based of different Convolutional Neural Network (CNN) models, fine-tuned on several datasets with the aim of exploiting their diversity. The aim of our study is to experiment the possibility of fine-tuning CNNs for underwater imagery analysis, the opportunity of using different datasets for pre-training models, the possibility to design an ensemble using the same architecture with small variations in the training procedure. Our experiments, performed on 5 well-known datasets (3 plankton and 2 coral datasets) show that the combination of such different CNN models in a heterogeneous ensemble grants a substantial performance improvement with respect to other state-of-the-art approaches in all the tested problems. One of the main contributions of this work is a wide experimental evaluation of famous CNN architectures to report the performance of both the single CNN and the ensemble of CNNs in different problems. Moreover, we show how to create an ensemble which improves the performance of the best single model. The MATLAB source code is freely link provided in title page. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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