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.
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- Near-infrared-based capsicum counting algorithm using YOLO11(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025) Mendez Meraz, Armando Enrico; Escobedo Cabello, Jesús Arturo; emimmayorquin; Cantoral Ceballos, Jose Antonio; School of Engineering and Sciences; Campus Monterrey; Gómez Espinosa, AlfonsoThis work presents a novel near-infrared-based approach to capsicum counting in greenhouses that uses the advantages of NIR imaging to enhance detection in challenging lighting condi- tions. The proposed algorithm integrates the YOLO11 detection model for capsicum iden-tification and the BoT-SORT multi-object tracker to track detections across a video stream, enabling accurate fruit counting. Trained on a dataset of 611 labeled images captured in a greenhouse, the detection model achieved an F1-score of 0.82, while the tracker obtained a multi-object tracking accuracy (MOTA) of 0.85. The results demonstrate the effectiveness of this NIR-based approach in automating fruit counting in greenhouse environments, offering potential applications in yield estimation.
- 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 SadaComputer 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.
- 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 SadaComputer 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.
- Deep learning for clothing classification, case study:thermal comfort(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-11-23) Medina Rosales, Adán; Ponce Cruz, Pedro; puemcuervo; López Caudana, Edgar Omar; Rojas Hernández, Mario; Soriano Avendaño, Luis Arturo; School of Engineering and Sciences; Campus Ciudad de México; Molina Gutiérrez, ArturoImage classification algorithm has being in quick development over the last 10 years with a new algorithm appearing every year, this new algorithms aim to be faster and more accurate than its predecessors, so real time implementations for object classifiers are more frequent. However the solutions for problems are going to more complex problems leaving things such as clothing ensemble classification on the side. There are some proposed solutions on the recognition of clothing garments but all aim to a specific solution in the fashion industry for customer categorization or shopping proposals, however a more general approach which recognizes multiple clothing garments is missing, and a real time clothing ensemble detection could be implemented in several problems. One of such problems is the case study for this project were a CNN implementation is used in video testing to propose the solution for clothing insulation determination using the real time clothing ensemble detector and therefore have a more accurate thermal comfort value. The results proved that the implementation of the chosen CNN architecture could be used as a clothing ensemble detector in a real time implementation, however since a minimized version of the needed dataset was used to verify the viability of this proposal a more complete dataset needs to be created in order to improve the models performance. In general this proposal shows the comparison between come CNN architectures and the datasets available for the propose objectives, as well as the creation of a new dataset that can be successfully used to train the chosen CNN model and produce a real time clothing ensemble detector.