Navigation visuelle d'un robot mobile dans un environnement d'extérieur semi-structuré

Cette thèse porte sur le traitement automatique d'images couleur, et son application à la robotique dans des environnements semi-structurés d'extérieur. Nous proposons une méthode de navigation visuelle pour des robots mobiles en utilisant une caméra couleur. Les domaines d'application de ce travail se situent dans l'automatisation de machines agricoles, en vue de la navigation automatique dans un réseau de chemins (pour aller d'une ferme à un champ par exemple). Nous présentons tout d'abord une analyse des principaux travaux de recherche dans la littérature sur la navigation visuelle. Une chaîne de pré-traitement pour le rendu couleur d'images numériques mono-capteur dotées d'un filtre Bayer est présentée ; elle se base sur une étude des techniques de démosaïquage, le calibrage chromatique d'images (balance de blancs) et la correction gamma. Une méthode d'interprétation monoculaire de la scène courante permet d'extraire les régions navigables et un modèle 2D de la scène. Nous traitons de la segmentation d'une image couleur en régions, puis de la caractérisation de ces régions par des attributs de texture et de couleur, et enfin, de l'identification des diverses entités de la scène courante (chemin, herbe, arbre, ciel, champ labouré,. . . ). Pour cela, nous exploitons deux méthodes de classification supervisée : la méthode de Support Vector Machine (SVM) et celle des k plus proches voisins (k-PPV). Une réduction d'information redondante par une analyse en composantes indépendantes (ACI) a permis d'améliorer le taux global de reconnaissance. Dans un réseau de chemins, le robot doit reconnaître les intersections de chemins lui permettant (a) dans une phase d'apprentissage, de construire un modèle topologique du réseau dans lequel il va devoir se déplacer et (b) dans une phase de navigation, de planifier et exécuter une trajectoire topologique définie dans ce réseau. Nous proposons donc une méthode de détection et classification du chemin : ligne droite, virage gauche, virage droite, carrefour en X, en T ou en Y. Une approche pour la représentation de la forme et de la catégorisation des contours (Shape Context) est utilisée à cet effet. Une validation a été effectuée sur une base d'images de routes ou chemins de campagne. En exploitant cette méthode pour détecter et classifier les noeuds du réseau de chemins, un modèle topologique sous forme d'un graphe est construit ; la méthode est validée sur une séquence d'images de synthèse. Enfin, dans la dernière partie de la thèse, nous décrivons des résultats expérimentaux obtenus sur le démonstrateur Dala du groupe Robotique et IA du LAAS-CNRS. Le déplacement du robot est contrôlé et guidé par l'information fournie par le système de vision à travers des primitives de déplacement élémentaires (Suivi-Chemin, Suivi-Objet, Suivi-Bordure,. . . ). Le robot se place au milieu du chemin en construisant une trajectoire à partir du contour de cette région navigable. étant donné que le modèle sémantique de la scène est produit à basse fréquence (de 0,5 à 1 Hz) par le module de vision couleur, nous avons intégré avec celui-ci, un module de suivi temporel des bords du chemin (par Snakes), pour augmenter la fréquence d'envoi des consignes (de 5 à 10 Hz) au module de locomotion. Modules de vision couleur et de suivi temporel doivent être synchronisés de sorte que le suivi puisse être réinitialisé en cas de dérive. Après chaque détection du chemin, une trajectoire sur le sol est planifiée et exécutée ; les anciennes consignes qui ne sont pas encore exécutées sont fusionnées et filtrées avec les nouvelles, donnant de la stabilité au système. ABSTRACT : This thesis deals with the automatic processing of color images, and its application to robotics in outdoor semi-structured environments. We propose a visual-based navigation method for mobile robots by using an onboard color camera. The objective is the robotization of agricultural machines, in order to navigate automatically on a network of roads (to go from a farm to a given field). Firstly, we present an analysis of the main research works about visual-based navigation literature. A preprocessing chain for color rendering on mono-sensor digital images equipped with a Bayer filter, is presented ; it is based on the analysis of the demosaicking techniques, the chromatic calibration of images (white point balance) and the correction gamma. Our monocular scene interpretation method makes possible to extract the navigable regions and a basic 2D scene modeling. We propose functions for the segmentation of the color images, then for the characterization of the extracted regions by texture and color attributes, and at last, for their classification in order to recognize the road and other entities of the current scene (grass, trees, clouds, hedges, fields,. . . ). Thus, we use two supervised classification methods : Support Vector Machines (SVM) and k nearest neighbors (k-NN). A redundancy reduction by using independent components analysis (ICA) was performed in order to improve the overall recognition rate. In a road network, the robot needs to recognize the roads intersections in order to navigate and to build a topological model from its trajectory. An approach for the road classification is proposed to recognize : straight ahead, turn-left, turn-right, road intersections and road bifurcations. An approach based on the road shape representation and categorization (shape context) is used for this purpose. A validation was carried out on an image dataset of roads or country lanes. By exploiting this method to detect and classify the nodes of a road network, a topological model based on a graph is built ; the method is validated on a sequence of synthetic images. Finally, Robot displacement is controlled and guided by the information provided by the vision system through elementary displacement primitives (Road-Follow, Follow-Object, Follow-Border,. . . ). Robot Dala is placed in the middle of the road by computing a trajectory obtained from the navigable region contours. As retrieving semantic information from vision is computationally demanding (low frequency 0.5 ¼ 1 Hz), a Snakes tracking process was implemented to speed up the transfer of instructions (5 ¼ 10 Hz) to the locomotion module. Both tasks must be synchronized, so the tracking can be re-initialized if a failure is detected. Locomotion tasks are planned and carried out while waiting for new data from the vision module ; the instructions which are not yet carried out, are merged and filtered with the new ones, which provides stability to the system

Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. Methods: The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. Findings: Global DALYs increased from 2·63 billion (95% UI 2·44–2·85) in 2010 to 2·88 billion (2·64–3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7–17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8–6·3) in 2020 and 7·2% (4·7–10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0–234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7–198·3]), neonatal disorders (186·3 million [162·3–214·9]), and stroke (160·4 million [148·0–171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3–51·7) and for diarrhoeal diseases decreased by 47·0% (39·9–52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54–1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5–9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0–19·8]), depressive disorders (16·4% [11·9–21·3]), and diabetes (14·0% [10·0–17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7–27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6–63·6) in 2010 to 62·2 years (59·4–64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6–2·9) between 2019 and 2021. Interpretation: Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. Funding: Bill & Melinda Gates Foundation

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Navigation visuelle d'un robot mobile dans un environnement d'extérieur semi-structuré

Cette thèse porte sur le traitement automatique d'images couleur, et son application à la robotique dans des environnements semi-structurés d'extérieur. Nous proposons une méthode de navigation visuelle pour des robots mobiles en utilisant une caméra couleur. Les domaines d'application de ce travail se situent dans l'automatisation de machines agricoles, en vue de la navigation automatique dans un réseau de chemins (pour aller d'une ferme à un champ par exemple ) [...]This thesis deals with the automatic processing of color images, and its application to robotics in outdoor semi-structured environments. We propose a visual-based navigation method for mobile robots by using an onboard color camera. The objective is the robotization of agricultural machines, in order to navigate automatically on a network of roads (to go from a farm to a given field) [...]TOULOUSE-ENSEEIHT (315552331) / SudocGRENOBLE-INRIA Rhône-Alpes (383972301) / SudocSudocFranceF

Robot Visual Navigation in Semi-structured Outdoor Environments

International audienceThis work describes a navigation framework for robots in semi-structured outdoor environments which enables planning of semantic tasks by chaining of elementary visual-based movement primitives. Navigation is achieved by understanding the underlying world behind the image and using these results as a guideline to control the robot. As retrieving semantic information from vision is computationally demanding, short-term tasks are planned and executed while new vision information is processed. Thanks to learning techniques, the methods are adapted to different environment conditions. Fusion and filtering techniques provide reliability and stability to the system. The procedures have been fully integrated and tested with a real robot in an experimental environment. Results are discussed

LRSE-Net: Lightweight Residual Squeeze-and-Excitation Network for Stenosis Detection in X-ray Coronary Angiography

Coronary heart disease is the primary cause of death worldwide. Among these, ischemic heart disease and stroke are the most common diseases induced by coronary stenosis. This study presents a Lightweight Residual Squeeze-and-Excitation Network (LRSE-Net) for stenosis classification in X-ray Coronary Angiography images. The proposed model employs redundant kernel deletion and tensor decomposition by Depthwise Separable Convolutions to reduce the model parameters up to 48.6 x concerning a Vanilla Residual Squeeze-and-Excitation Network. Furthermore, the reduction ratios of each Squeeze-and-Excitation module are optimized individually to improve the feature recalibration. Experimental results for Stenosis Detection on the publicly available Deep Stenosis Detection Dataset and Angiographic Dataset demonstrate that the proposed LRSE-Net achieves the best Accuracy—0.9549/0.9543, Sensitivity—0.6320/0.8792, Precision—0.5991/0.8944, and F1-score—0.6103/0.8944, as well as competitive Specificity of 0.9620/0.9733

Transfer Learning for Stenosis Detection in X-ray Coronary Angiography

Coronary artery disease is the most frequent type of heart disease caused by an abnormal narrowing of coronary arteries, also called stenosis or atherosclerosis. It is also the leading cause of death globally. Currently, X-ray Coronary Angiography (XCA) remains the gold-standard imaging technique for medical diagnosis of stenosis and other related conditions. This paper presents a new method for the automatic detection of coronary artery stenosis in XCA images, employing a pre-trained (VGG16, ResNet50, and Inception-v3) Convolutional Neural Network (CNN) via Transfer Learning. The method is based on a network-cut and fine-tuning approach. The optimal cut and fine-tuned layers were selected following 20 different configurations for each network. The three networks were fine-tuned using three strategies: only real data, only artificial data, and artificial with real data. The synthetic dataset consists of 10,000 images (80% for training, 20% for validation) produced by a generative model. These different configurations were analyzed and compared using a real dataset of 250 real XCA images (125 for testing and 125 for fine-tuning), regarding their randomly initiated CNNs and a fourth custom CNN, trained as well with artificial and real data. The results showed that pre-trained VGG16, ResNet50, and Inception-v3 cut on an early layer and fine-tuned, overcame the referencing CNNs performance. Specifically, Inception-v3 provided the best stenosis detection with an accuracy of 0.95, a precision of 0.93, sensitivity, specificity, and F1 score of 0.98, 0.92, and 0.95, respectively. Moreover, a class activation map is applied to identify the high attention regions for stenosis detection