Ciencias Exactas y Ciencias de la Salud

Permanent URI for this collectionhttps://hdl.handle.net/11285/551039

Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.

Browse

Search Results

Now showing 1 - 3 of 3
  • Tesis de maestría / master thesis
    Deep learning applied to the detection of traffic signs in embedded devices
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) Rojas García, Javier; Fuentes Aguilar, Rita Quetziquel; emimmayorquin; Morales Vargas, Eduardo; Izquierdo Reyes, Javier; School of Engineering and Sciences; Campus Eugenio Garza Sada
    Computer vision is an integral component of autonomous vehicle systems, enabling tasks such as obstacle detection, road infrastructure recognition, and pedestrian identification. Autonomous agents must perceive their environment to make informed decisions and plan and control actuators to achieve predefined goals, such as navigating from point A to B without incidents. In recent years, there has been growing interest in developing Advanced Driving Assistance Systems like lane-keeping assistants, emergency braking mechanisms, and traffic sign detection systems. This growth is driven by advancements in Deep Learning techniques for image processing, enhanced hardware capabilities for edge computing, and the numerous benefits promised by autonomous vehicles. This work investigates the performance of three recent and popular object detectors from the YOLO series (YOLOv7, YOLOv8, and YOLOv9) on a custom dataset to identify the optimal architecture for TSD. The objective is to optimize and embed the best-performing model on the Jetson Orin AGX platform to achieve real-time performance. The custom dataset is derived from the Mapillary Traffic Sign Detection dataset, a large-scale, diverse, and publicly available resource. Detection of traffic signs that could potentially impact the longitudinal control of the vehicle is focused on. Results indicate that YOLOv7 offers the best balance between mean Average Precision and inference speed, with optimized versions running at over 55 frames per second on the embedded platform, surpassing by ample margin what is often considered real-time (30 FPS). Additionally, this work provides a working system for real-time traffic sign detection that could be used to alert unattentive drivers and contribute to reducing car accidents. Future work will explore further optimization techniques such as quantization-aware training, conduct more thorough real-life scenario testing, and investigate other architectures, including vision transformers and attention mechanisms, among other proposed improvements.
  • Tesis de maestría / master thesis
    Deep learning applied to the detection of traffic signs in embedded devices
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-06) Rojas García, Javier; Fuentes Aguilar, Rita Quetziquel; emimmayorquin; Morales Vargas, Eduardo; Izquierdo Reyes, Javier; School of Engineering and Sciences; Campus Eugenio Garza Sada
    Computer vision is an integral component of autonomous vehicle systems, enabling tasks such as obstacle detection, road infrastructure recognition, and pedestrian identification. Autonomous agents must perceive their environment to make informed decisions and plan and control actuators to achieve predefined goals, such as navigating from point A to B without incidents. In recent years, there has been growing interest in developing Advanced Driving Assistance Systems like lane-keeping assistants, emergency braking mechanisms, and traffic sign detection systems. This growth is driven by advancements in Deep Learning techniques for image processing, enhanced hardware capabilities for edge computing, and the numerous benefits promised by autonomous vehicles. This work investigates the performance of three recent and popular object detectors from the YOLO series (YOLOv7, YOLOv8, and YOLOv9) on a custom dataset to identify the optimal architecture for TSD. The objective is to optimize and embed the best-performing model on the Jetson Orin AGX platform to achieve real-time performance. The custom dataset is derived from the Mapillary Traffic Sign Detection dataset, a large-scale, diverse, and publicly available resource. Detection of traffic signs that could potentially impact the longitudinal control of the vehicle is focused on. Results indicate that YOLOv7 offers the best balance between mean Average Precision and inference speed, with optimized versions running at over 55 frames per second on the embedded platform, surpassing by ample margin what is often considered real-time (30 FPS). Additionally, this work provides a working system for real-time traffic sign detection that could be used to alert unattentive drivers and contribute to reducing car accidents. Future work will explore further optimization techniques such as quantization-aware training, conduct more thorough real-life scenario testing, and investigate other architectures, including vision transformers and attention mechanisms, among other proposed improvements.
  • Tesis de maestría
    Pre-diagnosis of diabetic retinopathy implementing supervised learning algorithms using an ocular fundus Latin-American dataset for cross-data validation
    (Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-02) De la Cruz Espinosa, Emanuel; FUENTES AGUILAR, RITA QUETZIQUEL; 229297; Fuentes Aguilar, Rita Quetziquel; emipsanchez; García González, Alejandro; Ochoa Ruiz, Gilberto; Abaunza González, Hernán; School of Engineering and Sciences; Campus Monterrey
    Nowadays diabetes is a disease with worldwide presence and high mortality rate, causing a big social and economic impact. One of the major negative effects of diabetes is visual loss due to diabetic retinopathy (DR). To prevent this condition is necessary to identify referable patients by screening for DR, and complementing with an Optic Coherence Tomography (OCT), that is another study to perform an early detection of blindness doing several longitudinal scans at a series of lateral locations to generate a map of reflection sites in the sample and display it as a two-dimensional image achieving transmission images in turbid tissue. Regrettably the number of ophthalmologists and OCT devices is not enough to provide an adequate health care to the diabetic population. Although there exist AI systems capable of do DR screening, they do not aim the assessment specifically in macula area considering visible and proliferated anomalies, signs of high damage and late intervention. This work presents three surpevised machine learnig algorithms; a Random Forest (RF) classifier, a Convolutional Neural Network (CNN) model, and a transfer learning (TL) pretrained model able to sort fundus images in three classes as an fundus images exclusive database is labeled. Processing techniques such as channel splitting, color space transforms, histogram and spatial based filters and data augmentation are used in order to detect presence of diabetic retinopathy. The stages of this work are: Publicly available dataset debugging, macular segmentation and cropping, data pre-processing, features extraction, model training, test and validation performance evaluation with a exclusive Latin-American dataset considering accuracy, sensitivity and specificity as metrics. The best results achieved are a 61.22% of accuracy, 86.67% of sensitivity and 89.47% of specificity.
En caso de no especificar algo distinto, estos materiales son compartidos bajo los siguientes términos: Atribución-No comercial-No derivadas CC BY-NC-ND http://www.creativecommons.mx/#licencias
logo

El usuario tiene la obligación de utilizar los servicios y contenidos proporcionados por la Universidad, en particular, los impresos y recursos electrónicos, de conformidad con la legislación vigente y los principios de buena fe y en general usos aceptados, sin contravenir con su realización el orden público, especialmente, en el caso en que, para el adecuado desempeño de su actividad, necesita reproducir, distribuir, comunicar y/o poner a disposición, fragmentos de obras impresas o susceptibles de estar en formato analógico o digital, ya sea en soporte papel o electrónico. Ley 23/2006, de 7 de julio, por la que se modifica el texto revisado de la Ley de Propiedad Intelectual, aprobado

DSpace software copyright © 2002-2026

Licencia