Morales, RubénCharles Garza, Daniel2025-04-042023Charles Garza, D. (2023). Component Detection based on Mask R CNN. [Tesis maestría]. Instituto Tecnológico y de Estudios Superiores de Monterrey. Recuperdo de: https://hdl.handle.net/11285/703464https://hdl.handle.net/11285/703464https://orcid.org/0000-0003-0498-1566This 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.TextoengopenAccesshttp://creativecommons.org/licenses/by/4.0INGENIERÍA Y TECNOLOGÍA::CIENCIAS TECNOLÓGICAS::TECNOLOGÍA INDUSTRIAL::ESTUDIOS INDUSTRIALESScienceTesis de Maestría / master Thesis0009-0008-6561-7876Mask R-CNNDeep learningVision systemObject detectionObject segmentation1156969