Aplicación de técnicas de detección de objetos en imágenes mediante Deep Learning para la ayuda en la conducción en situaciones de tráfico complejas

Tesis inédita presentada en la Universidad Europea de Madrid. Escuela de Doctorado e Investigación. Programa de Doctorado en Ingeniería de Control y Sistemas Inteligentes para la Salud y el Medio AmbienteEl aprendizaje automático es una disciplina de la inteligencia artificial que ha cobrado importancia en los últimos años, resultando una pieza clave en multitud de investigaciones. Entre otros, ha sido el gran posibilitador de la conducción autónoma. Los vehículos autónomos, aún en desarrollo, son aquellos que son capaces de conducirse a sí mismos sin intervención humana. Para ello, se apoyan en los llamados ADAS, sistemas avanzados de ayuda a la conducción, con los que perciben el entorno para tomar decisiones. La hipótesis que ha guiado la presente tesis doctoral ha sido la de emplear técnicas de Deep Learning, para desarrollar nuevos sistemas de ayuda que informen al conductor de situaciones de la vía. Esto se ha realizado sobre dos casos de uso. El primero, un detector de paneles luminosos de mensaje variable o VMS, que toma instantáneas de la carretera, los localiza, y locuta su contenido. Su funcionamiento está basado en un modelo de aprendizaje profundo, y de un pipeline que procesa la imagen, extrayendo el texto mediante el modelo de reconocimiento óptico de caracteres y reproduciendo el contenido mediante el servicio de la nube. El segundo, un analizador de rotondas circulares de España que, mediante imágenes aéreas, reconoce los distintos carriles y vehículos y extrae información acerca de su estado. Para ello, este sistema encadena varios algoritmos de visión por computador para reconocer las circunferencias de los carriles y un modelo de aprendizaje profundo que detecta los distintos tipos de vehículos.Machine learning is a discipline of artificial intelligence that has gained importance in recent years, becoming a key clement in a multitude of research projects. It has been, among others, the great enabler of autonomous driving. Autonomous vehicles, which are still under development, are those that can drive themselves without human intervention. For this purpose, they rely on the so-called ADAS, advanced driving assistance systems, with which they perceive the environment to make decisions. The hypothesis that has guided this doctoral thesis has been to use deep learning techniques to develop new assistance systems that inform the driver of situations on the road. This has been applied to two use cases. The first one, a variable message sign (VMS) detector, which takes images of the road, locates them, and announces their content. It is based on a deep learning model and a pipeline that processes the image, extracting the text using the optical character recognition model and reproducing the content using a cloud service. The second, an analyzer of circular Spanish roundabouts that, using aerial images, recognizes the different lanes and vehicles and extracts information about their status. This system combines several computer vision algorithms to recognize the circumferences of the lanes and a deep learning model that detects the different types of vehicles.UE

Range Contraction and Population Decline of the European Dupont’s Lark Population

The Dupont’s lark (Chersophilus duponti) is an endangered passerine typical of Mediterranean shrub-steppes, whose European distribution is restricted to Spain. Here, we update the population size and distribution range of the species at a European scale and evaluate (i) the current status; (ii) the change in population size and distribution range of the species from 2004 to 2009 to the current period (2017–2022); and (iii) the effectiveness of the current network of special protection areas (SPAs) for protecting the Dupont’s lark. The European Dupont’s lark population showed a decrease of 29.9%, declining from ca. 3267 to 2289 territorial males from 2004 to 2009 to the current period. Moreover, the species has suffered a contraction in its distribution range of 35.9%, with only 39.3% of the species’ territories located within the current network of SPAs. Our findings agree with the previously described decline of the Dupont’s lark in Europe. The population decline was even larger in peripheral regions, which suggests that the species is suffering a centripetal process of contraction and extinction. These results indicate that if there is no change in present-day declining forces, several peripheral populations will reach extinction in a few decades and the overall population size of the species will continue decreasing. Our study should be considered as a last call for action and used for implementing urgent conservation measures to protect the species and its habitat. Future studies should focus on analyzing and managing the factors driving the species’ extinction and future actions for the conservation of the species should focus on increasing the percentage of the Dupont’s lark territories within protected areas, since the data are alarmingly low for a species that is facing clear risk of extinction

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AimThe SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery.MethodsThis was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin.ResultsOverall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P ConclusionOne in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease