2024
Autores
Fernandes, DS; Bispo, J; Bento, LC; Figueiredo, M;
Publicação
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT II
Abstract
Over the years, many solutions have been suggested in order to improve object detection in maritime environments. However, none of these approaches uses flight information, such as altitude, camera angle, time of the day, and atmospheric conditions, to improve detection accuracy and network robustness, even though this information is often available and captured by the UAV. This work aims to develop a network unaffected by image-capturing conditions, such as altitude and angle. To achieve this, metadata was integrated into the neural network, and an adversarial learning training approach was employed. This was built on top of the YOLOv7, which is a state-of-the-art realtime object detector. To evaluate the effectiveness of this methodology, comprehensive experiments and analyses were conducted. Findings reveal that the improvements achieved by this approach are minimal when trying to create networks that generalize more across these specific domains. The YOLOv7 mosaic augmentation was identified as one potential responsible for this minimal impact because it also enhances the model's ability to become invariant to these image-capturing conditions. Another potential cause is the fact that the domains considered (altitude and angle) are not orthogonal with respect to their impact on captured images. Further experiments should be conducted using datasets that offer more diverse metadata, such as adverse weather and sea conditions, which may be more representative of real maritime surveillance conditions. The source code of this work is publicly available at https://git hub.com/ipleiria-robotics/maritime-metadata-adaptation.
2024
Autores
Teixeira, CM; T. Ribeiro, PA; Vasconcelos-Raposo, J;
Publicação
PSYCHTECH & HEALTH JOURNAL
Abstract
2024
Autores
Vasconcelos-Raposo, J;
Publicação
PSYCHTECH & HEALTH JOURNAL
Abstract
2024
Autores
Vasconcelos-Raposo, J;
Publicação
PSYCHTECH & HEALTH JOURNAL
Abstract
2024
Autores
Vasconcelos-Raposo, J; Palumbo, J; Carvalho, A; Borges, J; M. Teixeira, C;
Publicação
PSYCHTECH & HEALTH JOURNAL
Abstract
2024
Autores
Matos, B; Garcia, JE; Correia, F;
Publicação
INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2022, ICNAAM-2022
Abstract
After the pandemic we experienced, companies have felt the need to reinvent themselves and adapt to the present moment. The Internet and social networks have developed and increased their activity substantially. Users spend more time on social networks, shop more online, and feel more than ever a need for information and to view content. The main objective of this research is to define and implement a content marketing strategy for the social networks, through a quarterly content plan in the marketing services company Naive. In the first part of the research, presented in this paper, the work consisted of designing and implementing a questionnaire, obtaining a sample of 200 respondents to assess their perceptions and habits regarding social networks and the content offered on social networks, to study the results. The results obtained and analysis done will be used to develop a content strategy for Naive, which include studying the specific objectives for the company's different social networks, the actions to be developed and the content to be implemented.
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