Ciencias Exactas y Ciencias de la Salud

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Development of mobile crowd sensing based models for fire risk assessments in constrained devices
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2025-06) Low Castro, Jesús Antonio; Rodríguez Hernandez, Gerardo; emimmayorquin; Gonzalez Mendoza, Miguel; Sanchez Ante, Gildardo; School of Engineering and Sciences; Campus Monterrey; Ochoa Ruiz, Gilberto
Wildfires have become one of the most critical challenges to address due to their increasing frequency as a result of climate change, causing significant damage to ecosystems, lives, and property. Although various strategies have been explored for wildfire management, a promising approach focuses on wildfire risk assessment through fuel identification, where fuels are sources of stored potential energy that combust under specific environmental and physical conditions. Since fuels are key determinants of fire behavior, identifying fire-prone areas inadvance can help reduce the severity, spread, and intensity of wildfires. Traditional fuel mapping techniques are commonly used for this purpose and rely primarily on satellite and aerial imagery, but face limitations in resolution, cost, and real-time accessibility, highlighting the need for complementary ground-based systems. This thesis explores a wildfire risk assessment approach based on ground-level fuel identification using computer vision models deployed on resource-constrained devices, specifically smartphones. To enable distributed data collection and inference, a mobile crowdsensing scheme is proposed. The methodology includes training, quantization, and deployment of object detection and semantic segmentation models for fuel identification on mobile devices. The research includes case studies on optimized object detection using the Edmonton Wildland-Urban Interface dataset, the deployment of lightweight semantic segmentation models using a custom dataset from the Arteaga Mountain Range in Mexico, and a semisupervised labeling strategy that uses a robust semantic segmentation model to augment training data. The results demonstrate that the proposed models achieve high accuracy while meeting the computational and storage constraints of mobile devices, supporting the feasibility of using mobile crowd-sensing and optimized vision models for a low-cost real-time assessment of wildfire risk.
Characterization of jet fire flame temperature zones using a deep learning-based segmentation approach
(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-12-02) Pérez Guerrero, Carmina; OCHOA RUIZ, GILBERTO; 352103; Ochoa Ruiz, Gilberto; puemcuervo; González Mendoza, Miguel; Mata Miquel, Christian; School of Engineering and Sciences; Campus Monterrey; Palacios Rosas, Adriana
Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants; however, they are usually involved in a process known as domino effect, that leads to more severe events, such as explosions or the initiation of another fire, making the analysis of such fires an important part of risk analysis. One such analysis would be the segmentation of different radiation zones within the flame, therefore this thesis presents an exploratory research regarding several traditional computer vision and Deep Learning segmentation approaches to solve this specific problem. A data set of propane jet fires is used to train and evaluate the different approaches. Different metrics are correlated to a manual ranking performed by experts to make an evaluation that closely resembles the expert’s criteria. Additionally, given the difference in the distribution of the zones and background of the images, different loss functions, that seek to alleviate data imbalance, are explored. The Hausdorff Distance and Adjusted Rand Index were the metrics with the highest correlation and the best results were obtained from training with a Weighted Cross-Entropy Loss. The best performing models were found to be the UNet architecture, along with its recent variations, Attention UNet and UNet++. These models are then used to segment a group of vertical jet flames of varying pipe outlet diameters to extract their main geometrical characteristics. Attention UNet obtained the best general performance in the approximation of both height and area of the flames, while also showing a statistically significant difference between it and UNet++. UNet obtained the best overall performance for the approximation of the lift-off distances; however, there is not enough data to prove a statistically significant difference between UNet and its two variations. The only instance where UNet++ outperformed the other models, was while obtaining the lift-off distances of the jet flames with 0.01275 m pipe outlet diameter. In general, the explored models show good agreement between the experimental and predicted values for relatively large turbulent propane jet flames, released in sonic and subsonic regimes; thus, making these radiation zones segmentation models, a suitable approach for different jet flame risk management scenarios.

Ciencias Exactas y Ciencias de la Salud

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