Abstract
Photo-identification is a widely used non-invasive technique in biological studies for understanding if a specimen has been seen multiple times only relying on specific unique visual characteristics. This information is essential to infer knowledge about the spatial distribution, site fidelity, abundance or habitat use of a species. Today there is a large demand for algorithms that can help domain experts in the analysis of large image datasets. For this reason, it is straightforward that the problem of identify and crop the relevant portion of an image is not negligible in any photo-identification pipeline. This paper approaches the problem of automatically cropping cetaceans images with a hybrid technique based on domain analysis and deep learning. Domain knowledge is applied for proposing relevant regions with the aim of highlighting the dorsal fins, then a binary classification of fin vs. no-fin is performed by a convolutional neural network. Results obtained on real images demonstrate the feasibility of the proposed approach in the automated process of large datasets of Risso’s dolphins photos, enabling its use on more complex large scale studies. Moreover, the results of this study suggest to extend this methodology to biological investigations of different species.
Highlights
Species monitoring is performed through the collection and the evaluation of meaningful bio-ecological parameters aimed to estimate, for example, their spatial distribution, site fidelity, abundance and migration as well as habitat use [1,2,3,4,5,6,7,8,9,10,11,12,13]
The work described in this paper addresses the problem of automatically cropping a dorsal fin starting from a full frame image using deep learning models
The methodology is defined as a deep hybrid model because it is inspired by region proposal networks but with the main characteristic of clearly splitting the region proposal task from the classification task demanded to a Convolutional Neural Network (CNN)
Summary
Species monitoring is performed through the collection and the evaluation of meaningful bio-ecological parameters aimed to estimate, for example, their spatial distribution, site fidelity, abundance and migration as well as habitat use [1,2,3,4,5,6,7,8,9,10,11,12,13] The estimation of these parameters can be greatly facilitated through the use of a non-invasive technique based on automated algorithms and a large data availability: the automatic photo-identification of specimens (photo-ID). Given the widespread diffusion of mobile devices and digital cameras able to capture an extremely high number of high quality images, the photo-ID of large amounts of data must be performed with the aid of automated or semi-automated approaches
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
