Automated Detection of Cetaceans in Their Natural Habitats via R-CNN and Computer Vision Techniques

Passive acoustic monitoring is essential for monitoring cetaceans in their natural habitats. In this paper, we consider the monitoring of Fin Whales (Balaenoptera physalus). The deployment of automated tools for this purpose is essential to efficiently handle and analyse the vast amounts of data generated by hundreds of hours of recordings, something humans cannot do. In this paper, we present two methods for automated detection systems based on a convolutional neural network (CNN) classifier and a circle detection technique. Both of them use a spectrogram of the recordings as input which converts sounds into images. The first method consists of a two-stage R-CNN classifier with 26 layers. The second method is an image-based technique that uses classical computer vision algorithms based on the morphology of pulses. Both approaches demonstrate good performance, with circle detection showing better results, even as it is a simpler method. The results obtained on a large dataset demonstrate that the proposed approach is highly effective in detecting and characterising animals in their habitats, thus offering valuable information to identify seasonal patterns.