ISEP - DM - Engenharia de Sistemas Computacionais Críticos
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Percorrer ISEP - DM - Engenharia de Sistemas Computacionais Críticos por assunto "Algoritmos de IA"
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- Development of an Intelligent and Efficient System for Monitoring and Optimising Sailboat PerformancePublication . TEIXEIRA, HENRIQUE MANUEL DE ALMEIDA E SILVA DOS SANTOS; Ferreira, Luis Miguel Moreira LinoThis thesis introduces an AI-powered tool that improves the analysis of sailing performance by automatically detecting thin, high-visibility stripes on sails. It uses computer vision and deep learning to extract key aerodynamic parameters, such as camber, draft, and twist. These parameters are essential for understanding sail shape and enhancing performance. The motivation lies in reducing reliance on traditional manual estimation methods while ensuring efficient onboard processing with lightweight devices like a GoPro camera connected to a tablet. The research starts with a review of current computer vision and AIbased image processing techniques. It also includes a sailing-specific look at the structural and aerodynamic features of sails. Several AI methods - Feature Extraction, Line Detection, Object Detection and Recognition, and Image Segmentation - are compared in this context. The analysis finds that semantic segmentation is the best technique for the goals of this thesis. A further comparison of semantic segmentation models - SegFormer, DeepLab, SAM, and Fast-SCNN - evaluated their accuracy, efficiency, and use for real-time deployment. This review shows that SegFormer is the most effective method for detecting lines in highresolution images of a sailboat's sail. The evaluation carried out in this thesis compares a traditional algorithm, developed in a previous thesis and reused here as a baseline, with an AI-based approach that uses the SegFormer model. This implementation relies on the SegFormer mit-b1 backbone, chosen for its balance between accuracy and efficiency. Mitb2 and mit-b3 were also tested for segmentation quality and processing time comparisons. The evaluation used a dataset of 23 videos and measured how well both methods could reliably detect lines for extracting aerodynamic parameters. The results show a clear tradeoff. The traditional method consistently produced faster processing times because it relies on lightweight operations optimised for CPU use. In contrast, the SegFormer model offered more accurate and reliable line segmentation but required more computational power. Among the tested backbones, SegFormer mit-b1 was the best choice, as mit-b2 and mit-b3 resulted in significantly longer processing times without substantial improvements in segmentation accuracy. In conclusion, the traditional algorithm is still beneficial when speed and limited resources are critical. However, the AI-based approach, especially with SegFormer mit-b1, stands out as a reliable and precise option when more computational resources are available. This work illustrates the potential to integrate AI-driven computer vision into sailing performance analysis, aiding in the accurate and automated extraction of aerodynamic parameters to enhance decision-making and performance improvement in sailing.