Ciencias Exactas y Ciencias de la Salud

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Component Detection based on Mask R CNN
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2023) Charles Garza, Daniel; Morales, Rubén; emimmayorquin; Vallejo Guevara, Antonio; Guedea Elizalde, Federico; Escuela de Ingeniería y Ciencias; Campus Monterrey
This thesis delves into the evolution and utilization of deep learning methodologies in the specific context of object detection and segmentation within the manufacturing industry. It thoroughly examines several state-of-the-art object detection techniques, including YOLO, RCNN, Fast R-CNN, etc. These methods are explored in detail, assessing their effectiveness and applicability in complex object identification and classification tasks. The study then focuses on Mask R-CNN, a method chosen for its outstanding performance in object segmentation and identification; especially, in cluttered and unstructured environments common in manufacturing settings.