A SIFT-based software system for the photo-identification of the Risso's dolphin

Environmental variables and machine learning models to predict cetacean abundance in the Central-eastern Mediterranean Sea

Although the Mediterranean Sea is a crucial hotspot in marine biodiversity, it has been threatened by numerous anthropogenic pressures. As flagship species, Cetaceans are exposed to those anthropogenic impacts and global changes. Assessing their conservation status becomes strategic to set effective management plans. The aim of this paper is to understand the habitat requirements of cetaceans, exploiting the advantages of a machine-learning framework. To this end, 28 physical and biogeochemical variables were identified as environmental predictors related to the abundance of three odontocete species in the Northern Ionian Sea (Central-eastern Mediterranean Sea). In fact, habitat models were built using sighting data collected for striped dolphins Stenella coeruleoalba, common bottlenose dolphins Tursiops truncatus, and Risso's dolphins Grampus griseus between July 2009 and October 2021. Random Forest was a suitable machine learning algorithm for the cetacean abundance estimation. Nitrate, phytoplankton carbon biomass, temperature, and salinity were the most common influential predictors, followed by latitude, 3D-chlorophyll and density. The habitat models proposed here were validated using sighting data acquired during 2022 in the study area, confirming the good performance of the strategy. This study provides valuable information to support management decisions and conservation measures in the EU marine spatial planning context.

Machine-Learning Approach for Automatic Detection of Wild Beluga Whales from Hand-Held Camera Pictures

A key aspect of ocean protection consists in estimating the abundance of marine mammal population density within their habitat, which is usually accomplished using visual inspection and cameras from line-transect ships, small boats, and aircraft. However, marine mammal observation through vessel surveys requires significant workforce resources, including for the post-processing of pictures, and is further challenged due to animal bodies being partially hidden underwater, small-scale object size, occlusion among objects, and distracter objects (e.g., waves, sun glare, etc.). To relieve the human expert's workload while improving the observation accuracy, we propose a novel system for automating the detection of beluga whales (Delphinapterus leucas) in the wild from pictures. Our system relies on a dataset named Beluga-5k, containing more than 5.5 thousand pictures of belugas. First, to improve the dataset's annotation, we have designed a semi-manual strategy for annotating candidates in images with single (i.e., one beluga) and multiple (i.e., two or more belugas) candidate subjects efficiently. Second, we have studied the performance of three off-the-shelf object-detection algorithms, namely, Mask-RCNN, SSD, and YOLO v3-Tiny, on the Beluga-5k dataset. Afterward, we have set YOLO v3-Tiny as the detector, integrating single- and multiple-individual images into the model training. Our fine-tuned CNN-backbone detector trained with semi-manual annotations is able to detect belugas despite the presence of distracter objects with high accuracy (i.e., 97.05 mAP@0.5). Finally, our proposed method is able to detect overlapped/occluded multiple individuals in images (beluga whales that swim in groups). For instance, it is able to detect 688 out of 706 belugas encountered in 200 multiple images, achieving 98.29% precision and 99.14% recall.

Behavioral Pattern of Risso’s Dolphin (Grampus griseus) in the Gulf of Taranto (Northern Ionian Sea, Central-Eastern Mediterranean Sea)

Relatively scant information is available on the Risso’s dolphin in comparison to the other species regularly present in the Mediterranean Sea. Recently, its conservation status has been updated to Endangered by the International Union for Conservation of Nature (IUCN) in this Sea. Therefore, the need to increase information on its biology and ecology is even more urgent. This study reports the first preliminary information on the behavioral traits of the species occurring in the Gulf of Taranto (Northern Ionian Sea). Data on predominant behavioral activity states and on a set of group composition variables (group formation, cruising speed, dive duration and interaction between individuals) were collected from April 2019 to September 2021, applying the focal-group protocol with instantaneous scan sampling. Group size, depth and group composition variables were compared between activity states. Results highlight that both the group size and the several variables considered varied significantly depending on activity state. The group size was significantly smaller during feeding than resting and traveling and a characterization in terms of group formation, cruise speed, dive duration and interaction between animals is provided for the different activity states. Moreover, a list of behavioral events which occurred, as well as their relative frequency of distribution among activity states, is reported. Finally, details on the sympatric occurrences between Risso’s and striped dolphins, as well as the repetitive interaction observed between adult individuals and plastic bags floating on the sea surface, are reported and discussed.

Personal Heart Health Monitoring Based on 1D Convolutional Neural Network

The automated detection of suspicious anomalies in electrocardiogram (ECG) recordings allows frequent personal heart health monitoring and can drastically reduce the number of ECGs that need to be manually examined by the cardiologists, excluding those classified as normal, facilitating healthcare decision-making and reducing a considerable amount of time and money. In this paper, we present a system able to automatically detect the suspect of cardiac pathologies in ECG signals from personal monitoring devices, with the aim to alert the patient to send the ECG to the medical specialist for a correct diagnosis and a proper therapy. The main contributes of this work are: (a) the implementation of a binary classifier based on a 1D-CNN architecture for detecting the suspect of anomalies in ECGs, regardless of the kind of cardiac pathology; (b) the analysis was carried out on 21 classes of different cardiac pathologies classified as anomalous; and (c) the possibility to classify anomalies even in ECG segments containing, at the same time, more than one class of cardiac pathologies. Moreover, 1D-CNN based architectures can allow an implementation of the system on cheap smart devices with low computational complexity. The system was tested on the ECG signals from the MIT-BIH ECG Arrhythmia Database for the MLII derivation. Two different experiments were carried out, showing remarkable performance compared to other similar systems. The best result showed high accuracy and recall, computed in terms of ECG segments and even higher accuracy and recall in terms of patients alerted, therefore considering the detection of anomalies with respect to entire ECG recordings.

A Novel Approach for the Automatic Estimation of the Ciliated Cell Beating Frequency

The qualitative and quantitative evaluation of nasal epithelial cells is interesting in chronic infectious and inflammatory pathologies of the nose and sinuses. Among the cells of the population of the nasal mucosa, ciliated cells are particularly important. In fact, the observation of these cells is essential to investigate primary ciliary dyskinesia, a rare and severe disease associated with other serious diseases such as respiratory diseases, situs inversus, heart disease, and male infertility. Biopsy or brushing of the ciliary mucosa and assessment of ciliary function through measurements of the Ciliary Beating Frequency (CBF) are usually required to facilitate diagnosis. Therefore, low-cost and easy-to-use technologies devoted to measuring the ciliary beating frequency are desirable. We have considered related works in this field and noticed that up to date an actually usable system is not available to measure and monitor CBF. Moreover, performing this operation manually is practically unfeasible or demanding. For this reason, we designed BeatCilia, a low cost and easy-to-use system, based on image processing techniques, with the aim of automatically measuring CBF. This system performs cell Region of Interest (RoI) detection basing on dense optical flow computation of cell body masking, focusing on the cilia movement and taking advantage of the structural characteristics of the ciliated cell and CBF estimation by applying a fast Fourier transform to extract the frequency with the peak amplitude. The experimental results show that it offers a reliable and fast CBF estimation method and can efficiently run on a consumer-grade smartphone. It can support rhinocytologists during cell observation, significantly reducing their efforts.

Combined Color Semantics and Deep Learning for the Automatic Detection of Dolphin Dorsal Fins

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.