2. Maestría
Permanent URI for this communityhttp://98.81.228.127/handle/20.500.12404/2
Tesis de la Escuela de Posgrado
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Item Application on semantic segmentation with few labels in the detection of water bodies from PERUSAT-1 satellite's images(Pontificia Universidad Católica del Perú, 2020-07-02) Gonzalez Villarreal, Jessenia Margareth Marina; Beltrán Castañón, César ArmandoRemote sensing is widely used to monitor earth surfaces with the main objective of extracting information from it. Such is the case of water surface, which is one of the most affected extensions when flood events occur, and its monitoring helps in the analysis of detecting such affected areas, considering that adequately defining water surfaces is one of the biggest problems that Peruvian authorities are concerned with. In this regard, semi automatic mapping methods improve this monitoring, but this process remains a time-consuming task and into the subjectivity of the experts. In this work, we present a new approach for segmenting water surfaces from satellite images based on the application of convolutional neural networks. First, we explore the application of a U-Net model and then a transfer knowledge-based model. Our results show that both approaches are comparable when trained using an 680-labelled satellite image dataset; however, as the number of training samples is reduced, the performance of the transfer knowledge-based model, which combines high and very high image resolution characteristics, is improvedItem Design of a mobile robot’s control system for obstacle identification and avoidance using sensor fusion and model predictive control(Pontificia Universidad Católica del Perú, 2017-10-14) Barreto Guerra, Jean Paul; Morán Cárdenas, Antonio Manuel; Hopfgarten, SiegbertThe aim of this master thesis is to design a control system based on model predictive control (MPC) with sensor data fusion for obstacle avoidance. Since the amount of obtained data is larger due to multiple sensors, the required sampling time has to be larger enough in comparison with the calculation time of the optimal problem. Then it is proposed a simplification of the mobile robot model in order to reduce this optimization time. The sensor data fusion technique uses the range information of a laser scanner and the data of a mono-camera acquired from image processing techniques. In image processing different detection algorithms are proposed such as shape and color detection. Therefore an estimation of the obstacles dimension and distance is explained obtaining accurate results. Finally a data fusion for obstacle determination is developed in order to use this information in the optimization control problem as a path constraint. The obtained results show the mobile robot behavior in trajectories tracking and obstacle avoidance problems by comparing two different sampling times. It is concluded that the mobile robot reaches the final desired position while avoiding the detected obstacles along the trajectory.