Improving Pantanal fish species recognition through taxonomic ranks in convolutional neural networks

• Published in: Ecological informatics Vol. 53; p. 100977
• Main Authors: dos Santos, Anderson Aparecido, Gonçalves, Wesley Nunes
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2019
• Subjects: Computer vision; Fish species recognition; Pantanal; Pantanal; Fish species recognition; Computer vision
• ISSN: 1574-9541
• DOI: 10.1016/j.ecoinf.2019.100977

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. •In this paper, we propose a new method for Pantanal fish species recognition.•Taxonomy (fish order and family) was included in convolutional neural networks.•Experiments were performed on a image dataset with 68 fish species.•Results using the taxonomy proved superior to the traditional approach.