Lightweight and efficient convolutional neural networks for recognition of dolphin dorsal fins |
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| Gianvito Losapio, Rosalia Maglietta, Tiziano Politi, Ettore Stella, Carmelo Fanizza, Karin Hartman, Roberto Carlucci, Giovanni Dimauro, Vito Renò |
- Abstract:
- The study of cetaceans is of vital importance to infer biological information useful to drive sustainable action plans aimed at preserving the marine environment and its biodiversity. In a recent study, we developed a novel algorithm for the detection of dorsal fins in the context of a fully automated pipeline for the photo-identification of Risso's dolphins. A lightweight convolutional neural network (CNN) architecture was proposed to recognize fins among cropped images, filtering the inputs for the photo-identification algorithm. In this paper, we compare the performances of that custom CNN to another extremely efficient architecture: Shufflenet. Training an efficient classifier is a key effort to speed up the first part of the photo-identification pipeline, enabling the feasibility of large scale ecological studies. The experiment confirms that both architectures provide a robust feature extraction capability for the problem in hand, even with a significantly smaller number of parameters with respect to other popular state-of-the-art CNNs.
- Download:
- IMEKO-TC19-MetroSea-2020-13.pdf
- DOI:
- -
- Event details
- IMEKO TC:
- TC19
- Event name:
- MetroSea 2020
- Title:
TC19 International Workshop on Metrology for the Sea
- Place:
- Naples, ITALY
- Time:
- 05 October 2020 - 07 October 2020