期刊
ECOLOGICAL INFORMATICS
卷 53, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.ecoinf.2019.100977
关键词
Fish species recognition; Pantanal; Computer vision
类别
资金
- FUNDECT - State of Mato Grosso do Sul Foundation to Support Education, Science and Technology
- CAPES - Brazilian Federal Agency for Support and Evaluation of Graduate Education
- CNPq - National Council for Scientific and Technological Development
Fish species recognition is an important task to preserve ecosystems, feed humans, and tourism. In particular, the Pantanal is a wetland region that harbors hundreds of species and is considered one of the most important ecosystems in the world. In this paper, we present a new method based on convolutional neural networks (CNNs) for Pantanal fish species recognition. A new CNN composed of three branches that classify the fish species, family and order is proposed with the aim of improving the recognition of species with similar characteristics. The branch that classifies the fish species uses information learned from the family and order, which has shown to improve the overall accuracy. Results on unrestricted image dataset showed that the proposed method provides superior results to traditional approaches. Our method obtained an accuracy of 0.873 versus 0.864 of traditional CNN in recognition of 68 fish species. In addition, our method provides fish family and order recognition, which obtained accuracies of 0.938 and 0.96, respectively. We hope that, with these promising results, an automatic tool can be developed to monitor species in an important region such as the Pantanal.
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