Navegando por Assunto "Computer vision"

Agora exibindo 1 - 6 de 6

Acesso aberto (Open Access)
Deep learning in education 5.0: proposing 3d geometric shapes classification model to improve learning on a metaverse application
(Universidade Federal do Pará, 2024-01-18) SANTOS, Adriano Madureira dos; SERUFFO, Marcos César da Rocha; http://lattes.cnpq.br/3794198610723464; https://orcid.org/0000-0002-8106-0560
The Brazilian educational system faces significant challenges, as evidenced by low educational development assessment scores. Due to the traditional educational model employed in the country, there are difficulties in the effective transmission of complex content, leading to high rates of academic failure and subsequent school dropout. The lack of innovation, especially in basic education settings, contributes to a scenario of low mathematical proficiency among Brazilian students. In this context, this work arises as a result of an innovation built to enhance the Geometa application, developed by the Inteceleri company, through the integration of Metaverse and Artificial Intelligence technologies to create an immersive and interactive educational environment. The intention is to train Artificial Intelligence for real-time three-dimensional geometric shape recognition from real-world object images. The proposal aims to mitigate challenges faced in Brazilian basic Mathematics education by adopting innovative technological approaches aligned with Education 5.0, which can be replicated for similar technologies involving the Metaverse. Furthermore, it is also intended to create a dynamic and sustainable educational environment that not only facilitates the mathematical concepts understanding but also promotes active student participation, encouraging their creativity and autonomy in the learning process. The method used relies on the ObjectNet dataset image reclassification from objects to three-dimensional geometric shapes. The reclassified images are used to train CNN, MobileNet, ResNet, ResNeXt, ViT and BEiT Deep Learning models, which are subsequently evalua ted through Machine Learning, inference time and dimension performance measures. Thus, the best-performance Artificial Intelligence model is selected for future integration into Geometa. As contributions of this work, the following were accomplished: (i) the defined models were trained for the three-dimensional geometric shapes recognition; (ii) the models were evaluated through Machine Learning, inference time and dimension performance measures; and (iii) the best-performance model was selected considering the highest assertiveness and smoothness based on models performances analysis. Concerning the obtained results, the ResNet surpassed BEiT, which was the second better performance model, in 5% Precision and 5 Inference Per Second. Finally, the ResNet model reached 84% Precision and 9 Inferences Per Second, being observed as the best-performance Artificial Intelligence for Geometa application integration flow.
Acesso aberto (Open Access)
Detecção de danos em superfícies geotécnicas com redes neurais convolucionais de baixa complexidade
(Universidade Federal do Pará, 2024-05-29) ARAÚJO, Thabatta Moreira Alves de; FRANCÊS, Carlos Renato Lisboa; ttp://lattes.cnpq.br/7458287841862567
Most natural disasters result from geodynamic events, such as landslides and collapse of geotechnical structures. These failures are catastrophic that directly impact the environment and cause financial and human losses. Visual inspection is the main method for detecting surface flaws in geotechnical structures. However, visits to the site can be risky due to the possibility of soil’s instability. Furthermore, the terrain design, hostile environment and remote installation conditions make access to these structures impractical. When a quick and safe assessment is necessary, computer vision analysis becomes a potential alternative. However, studies on computer vision techniques still need to be explored in this field due to the particularities of geotechnical engineering, such as limited, redundant and scarce public data sets. In this context, this thesis presents a redes neurais convolucionais, do inglês Convolutional Neural Network (CNN) approach for identifying defects on the surface of geotechnical structures to reduce dependence on human-led on-site inspections. To this end, images of surface failure indicators were collected on slopes on the banks of a Brazilian highway, with the help of UAVs and mobile devices. Next, low-complexity CNN architectures were explored to build a binary classifier capable of detecting flaws apparent to the naked human eye in images. The architecture composed of three convolutional layers, each with 32 filters, followed by two fully connected layers, each composed of 128 neurons and output with one neuron, showed an accuracy of 94.26%. The performance evaluation of the model with the test set obtained AUC metrics of 0.99, confusion matrix, and a AUPRC curve that indicates robust performance of the classifier in detecting damage, while maintaining a low computational complexity, making it suitable for applications field practices. The contributions of the thesis include the provision of an image database, the obtaining of a classification model suitable for scarce data and limited computational resources, and the exploration of strategies for remote inspection and detection of signs of failure in geotechnical structures.
Acesso aberto (Open Access)
Detecção e rastreamento de componentes de vagões ferroviários utilizando redes neurais convolucionais e restricões geométricas
(Universidade Federal do Pará, 2020-04-27) GONÇALVES, Camilo Lélis Assis; BARROS, Fabrício José Brito; http://lattes.cnpq.br/9758585938727609
A inspeção de componentes de trem que podem causar descarrilamento possui um papel importante na manutenção ferroviária. A fim de aumentar a produtividade e a segurança, empresas prestadoras de serviços procuram por soluções de inspeção automáticas e confiáveis. Apesar da inspeção automática baseada em visão computacional ser um conceito consolidado, tais aplicações desafiam a comunidade de desenvolvimento em razão de fatores ambientais e logísticos a serem considerados. Este trabalho propõe uma técnica de detecção e estimativa das posições das regiões de dreno presentes em vagões de trem. Nosso detector/rastreador consiste em uma rede neural convolucional e um conjunto de restrições geométricas, que levam em conta a trajetória ideal dos componentes de interesse dos vagões e as distâncias entre eles. Detalhamos os procedimentos de treinamento e validação, juntamente com as métricas utilizadas para aferir a performance do sistema proposto. Os resultados apresentados são comparados com outras duas técnicas, e exibem um bom custo‑benefício entre confiança e complexidade computacional para a detecção dos componentes de interesse.
Acesso aberto (Open Access)
Estimativa Volumétrica de Resíduos Sólidos Urbanos em Imagem de Visualização Única.
(Universidade Federal do Pará, 2024-09-02) AZANCORT NETO, Júlio Leite; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
Efficient solid waste management is crucial for keeping the city clean and sustainable. This work presents a methodology that uses well-established algorithms for volume estimation in urban solid waste management from single-view images. The proposed system is based on state-of-the-art computer vision concepts and models, including instance segmentation, depth estimation, and volume calculation based on point clouds. The methodology demonstrated the ability to accurately estimate the volume of both individual and multiple solid waste objects in images. We evaluated our approach using real-world data. Despite challenges such as manual rescaling of distances and limited datasets, our system shows considerable potential for refinement and improvement, targeting complex scenarios like real urban environments. Numerical results indicated that the proposed system is promising even in complex scenarios, with mean absolute percentage errors (MAPE) of 8.60% for single waste and 9.23% for multiple wastes, resulting in an overall average of 8.91%. The coefficient of determination was 95.11% for single instances and 87.64% for multiple instances. The proposed methodology significantly contributes to the advancement of management technologies in smart cities.
Acesso aberto (Open Access)
Identificacao de larvas de mosquitos do genero aedes utilizando redes neurais convolucionais
(Universidade Federal do Pará, 2023-09-29) SILVA, Romário da Costa; FERREIRA JÚNIOR, José Jailton Henrique; http://lattes.cnpq.br/9031636126268760; FRANCÊS, Carlos Renato Lisboa
Arboviruses transmitted by mosquitoes of the Aedes genus constitute a threat to public health. Detection and control of these vectors are critical to preventing disease outbreaks including Dengue, Chikungunya, Zika and Yellow Fever. Computer vision and deep learning techniques have been increasingly used in epidemiological control, mainly with regard to the classification and detection of these mosquitoes. In this sense, three models are proposed for classification, detection and segmentation of mosquito larvae based on the use of convolutional neural networks (CNN) and object detection algorithms (YOLO). For this purpose, a dataset was created for training purposes. The dataset is composed of images of larvae, being categorized between Aedes and Non-Aedes classes. The results show that the proposed models are promising strategies and achieved accuracy values of 86.71%, mAP (Mean Average Precision) of 88.3% and 95.7% for the tasks of classification, detection and segmentation, respectively.
Acesso aberto (Open Access)
Redes Neurais Convolucionais para Auxiliar no Diagnóstico de Exames Preventivo de Colo de Útero.
(Universidade Federal do Pará, 2024-09-18) COSTA, Edriane do Socorro Silva; FRANCÊS, Carlos Renato Lisboa; http://lattes.cnpq.br/7458287841862567
The cervical screening exam is a widely used method to detect cervical cancer and precancerous lesions. Automated classification of the results can assist healthcare professionals in accurately identifying abnormal cytology patterns, increasing accuracy and consistency in detecting anomalies. Furthermore, systematizing this solution can reduce analysis time and associated costs, enabling the provision of an immediate pre-diagnosis, especially in remote areas. This approach also has the potential for integration into public health systems, contributing to more efficient and accessible care. Therefore, this study proposes the application of pre-trained convolutional neural network models VGG16 and VGG19 for classifying images resulting from the liquid-based cytology technique, comparing the performance of 4-class versus 2-class classification with balanced and unbalanced data. Several architectures were tested, and accuracies of up to 98% were achieved, along with good classification metrics, showing potential as a solution to assist healthcare professionals in more assertive classification of these results.