Identificación, tallado y segmentación de especies de pesca mediante visión artificial y deep learning en entornos con alto solapamiento

En este trabajo de fin de máster se propone una continuación a un sistema para la identificación y segmentación automática de especies en lonja mediante visión por computador y deep learning. Se abordan propuestas para un tallado de especies y cálculo de biomasa en entornos calibrados y no calibrados, además de la adaptación del sistema a un dominio de lonja mayorista, con una propuesta de cambio de arquitectura de red para mejorar los resultados en el nuevo dominio. Los métodos propuestos se han probado con más de 3000 imágenes adquiridas en entornos reales de las lonjas del Campello y Altea, obteniendo resultados que permiten una implementación y un correcto desarrollo en un ambiente real. El presente trabajo pretende mejorar la gestión pesquera, contribuyendo a la preservación del ecosistema marino y favoreciendo la economía local

Few-shot learning for COVID-19 chest X-ray classification with imbalanced data: an inter vs. intra domain study

Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. However, some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. This work studies the effect of these challenges at the intra- and inter-domain level in few-shot learning scenarios with severe data imbalance. For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance. Specifically, different initialization and data augmentation methods are analyzed, and four adaptations to Siamese networks of solutions to deal with imbalanced data are introduced, including data balancing and weighted loss, both separately and combined, and with a different balance of pairing ratios. Moreover, we also assess the inference process considering four classifiers, namely Histogram, kNN, SVM, and Random Forest. Evaluation is performed on three chest X-ray datasets with annotated cases of both positive and negative COVID-19 diagnoses. The accuracy of each technique proposed for the Siamese architecture is analyzed separately. The results are compared to those obtained using equivalent methods on a state-of-the-art CNN, achieving an average F1 improvement of up to 3.6%, and up to 5.6% of F1 for intra-domain cases. We conclude that the introduced techniques offer promising improvements over the baseline in almost all cases and that the technique selection may vary depending on the amount of data available and the level of imbalance.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature

Efficient multi-task progressive learning for semantic segmentation and disparity estimation

Scene understanding is an important area in robotics and autonomous driving. To accomplish these tasks, the 3D structures in the scene have to be inferred to know what the objects and their locations are. To this end, semantic segmentation and disparity estimation networks are typically used, but running them individually is inefficient since they require high-performance resources. A possible solution is to learn both tasks together using a multi-task approach. Some current methods address this problem by learning semantic segmentation and monocular depth together. However, monocular depth estimation from single images is an ill-posed problem. A better solution is to estimate the disparity between two stereo images and take advantage of this additional information to improve the segmentation. This work proposes an efficient multi-task method that jointly learns disparity and semantic segmentation. Employing a Siamese backbone architecture for multi-scale feature extraction, the method integrates specialized branches for disparity estimation and coarse and refined segmentations, leveraging progressive task-specific feature sharing and attention mechanisms to enhance accuracy for solving both tasks concurrently. The proposal achieves state-of-the-art results for joint segmentation and disparity estimation on three distinct datasets: Cityscapes, TrimBot2020 Garden, and S-ROSeS, using only of the parameters of previous approaches.This work was supported by the I+D+i project TED2021-132103A-I00 (DOREMI), funded by MCIN/AEI /10.13039/501100011033

Identificación de especies de pesca artesanal mediante visión artificial y Deep learning

En este trabajo de fin de grado se propone un sistema para la identificación automática de especies en lonja. Se aborda la identificación por medio de técnicas de visión por computador e inteligencia artificial basadas en Deep Learning. Se analizan las principales especies pesqueras en el ámbito del LIC Cabo de Huertas de la región levantino balear utilizando como caso de estudio la lonja del Campello. Los métodos basados en Deep Learning permiten realizar predicciones a partir de conjuntos de datos cuya cantidad y calidad afectan a la precisión de la red neuronal. Otro aspecto a tener en cuenta es la selección y configuración del modelo de red para que aporte los mejores resultados posibles con el dataset disponible. El método propuesto se ha probado con más de 1000 imágenes adquiridas en situación real en la lonja del Campello, obteniendo tasas de clasificación que permitirán abordar el desarrollo de las siguientes fases del sistema. El resultado del trabajo contribuirá a mejorar la gestión pesquera optimizada al tener datos de las principales especies capturadas en tiempo real

Simultaneous, vision-based fish instance segmentation, species classification and size regression

Overexploitation of fisheries is a worldwide problem, which is leading to a large loss of diversity, and affects human communities indirectly through the loss of traditional jobs, cultural heritage, etc. To address this issue, governments have started accumulating data on fishing activities, to determine biomass extraction rates, and fisheries status. However, these data are often estimated from small samplings, which can lead to partially inaccurate assessments. Fishing can also benefit of the digitization process that many industries are undergoing. Wholesale fish markets, where vessels disembark, can be the point of contact to retrieve valuable information on biomass extraction rates, and can do so automatically. Fine-grained knowledge about the fish species, quantities, sizes, etc. that are caught can be therefore very valuable to all stakeholders, and particularly decision-makers regarding fisheries conservation, sustainable, and long-term exploitation. In this regard, this article presents a full workflow for fish instance segmentation, species classification, and size estimation from uncalibrated images of fish trays at the fish market, in order to automate information extraction that can be helpful in such scenarios. Our results on fish instance segmentation and species classification show an overall mean average precision (mAP) at 50% intersection-over-union (IoU) of 70.42%, while fish size estimation shows a mean average error (MAE) of only 1.27 cm.This work was developed with the collaboration of the Biodiversity Foundation (Spanish Ministry for the Ecological Transition and the Demographic Challenge), through the Pleamar Programme, co-financed by the European Maritime and Fisheries Fund (EMFF) Deepfish/Deepfish 2 projects. The European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 supported this research under the “CHAN-TWIN” project (grant TED2021-130890B-C21) and the HORIZON-MSCA-2021-SE-0 action number: 101086387, REMARKABLE, Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning

DeepFish project Yolact-based fish instance segmentation and species classification

<p>This is a retrained Yolact network for Fish instance segmentation and species classification.</p&gt

The DeepFish computer vision dataset for fish instance segmentation, classification, and size estimation

Preserving maritime ecosystems is a major concern for governments and administrations. Additionally, improving fishing industry processes, as well as that of fish markets, to have a more precise evaluation of the captures, will lead to a better control on the fish stocks. Many automated fish species classification and size estimation proposals have appeared in recent years, however, they require data to train and evaluate their performance. Furthermore, this data needs to be organized and labelled. This paper presents a dataset of images of fish trays from a local wholesale fish market. It includes pixel-wise (mask) labelled specimens, along with species information, and different size measurements. A total of 1,291 labelled images were collected, including 7,339 specimens of 59 different species (in 60 different class labels). This dataset can be of interest to evaluate the performance of novel fish instance segmentation and/or size estimation methods, which are key for systems aimed at the automated control of stocks exploitation, and therefore have a beneficial impact on fish populations in the long run.This work was developed with the collaboration of the Biodiversity Foundation (Spanish Ministry for the Ecological Transition and the Demographic Challenge), through the Pleamar Programme, co-financed by the European Maritime and Fisheries Fund (EMFF). Deepfish/Deepfish 2 projects

Jornadas Nacionales de Robótica y Bioingeniería 2023: Libro de actas

Las Jornadas de Robótica y Bioingeniería de 2023 tienen lugar en la Escuela Técnica Superior de Ingeniería Industrial de la Universidad Politécnica de IVIadrid, entre los días 14 y 16 de junio de 2023. En este evento propiciado por el Comité Español de Automática (CEA) tiene lugar la celebración conjunta de las XII Jornadas Nacionales de Robótica y el XIV Simposio CEA de Bioingeniería. Las Jornadas Nacionales de Robótica es un evento promovido por el Grupo Temático de Robótica (GTRob) de CEA para dar visibilidad y mostrar las actividades desarrolladas en el ámbito de la investigación y transferencia tecnológica en robótica. Asimismo, el propósito de Simposio de Bioingeniería, que cumple ahora su decimocuarta dicción, es el de proporcionar un espacio de encuentro entre investigadores, desabolladores, personal clínico, alumnos, industriales, profesionales en general e incluso usuarios que realicen su actividad en el ámbito de la bioingeniería. Estos eventos se han celebrado de forma conjunta en la anualidad 2023. Esto ha permitido aunar y congregar un elevado número de participantes tanto de la temática robótica como de bioingeniería (investigadores, profesores, desabolladores y profesionales en general), que ha posibilitado establecer puntos de encuentro, sinergias y colaboraciones entre ambos. El programa de las jornadas aúna comunicaciones científicas de los últimos resultados de investigación obtenidos, por los grupos a nivel español más representativos dentro de la temática de robótica y bioingeniería, así como mesas redondas y conferencias en las que se debatirán los temas de mayor interés en la actualidad. En relación con las comunicaciones científicas presentadas al evento, se ha recibido un total de 46 ponencias, lo que sin duda alguna refleja el alto interés de la comunidad científica en las Jornadas de Robótica y Bioingeniería. Estos trabajos serán expuestos y presentados a lo largo de un total de 10 sesiones, distribuidas durante los diferentes días de las Jornadas. Las temáticas de los trabajos cubren los principales retos científicos relacionados con la robótica y la bioingeniería: robótica aérea, submarina, terrestre, percepción del entorno, manipulación, robótica social, robótica médica, teleoperación, procesamiento de señales biológicos, neurorehabilitación etc. Confiamos, y estamos seguros de ello, que el desarrollo de las jornadas sea completamente productivo no solo para los participantes en las Jornadas que podrán establecer nuevos lazos y relaciones fructíferas entre los diferentes grupos, sino también aquellos investigadores que no hayan podido asistir. Este documento que integra y recoge todas las comunicaciones científicas permitirá un análisis más detallado de cada una de las mismas

Diminishing benefits of urban living for children and adolescents’ growth and development

Optimal growth and development in childhood and adolescence is crucial for lifelong health and well-being1–6. Here we used data from 2,325 population-based studies, with measurements of height and weight from 71 million participants, to report the height and body-mass index (BMI) of children and adolescents aged 5–19 years on the basis of rural and urban place of residence in 200 countries and territories from 1990 to 2020. In 1990, children and adolescents residing in cities were taller than their rural counterparts in all but a few high-income countries. By 2020, the urban height advantage became smaller in most countries, and in many high-income western countries it reversed into a small urban-based disadvantage. The exception was for boys in most countries in sub-Saharan Africa and in some countries in Oceania, south Asia and the region of central Asia, Middle East and north Africa. In these countries, successive cohorts of boys from rural places either did not gain height or possibly became shorter, and hence fell further behind their urban peers. The difference between the age-standardized mean BMI of children in urban and rural areas was <1.1 kg m–2 in the vast majority of countries. Within this small range, BMI increased slightly more in cities than in rural areas, except in south Asia, sub-Saharan Africa and some countries in central and eastern Europe. Our results show that in much of the world, the growth and developmental advantages of living in cities have diminished in the twenty-first century, whereas in much of sub-Saharan Africa they have amplified