Uma solução de baixo custo para o processamento de imagens aéreas obtidas por Veículos Aéreos Não Tripulados

• Ano de defesa: 2016
• Autor(a) principal: Silva, Jonas Fernandes da
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal da Paraíba; Brasil; Informática; Programa de Pós-Graduação em Informática; UFPB
• Palavras-chave em Português: Monitoramento agrícola; Sistemas embarcados; OpenCV; 1-NN; Watershed; Agricultural Monitoring; Embedded systems; CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufpb.br/jspui/handle/tede/9262

Currently, unmanned aerial vehicles (UAV) are increasingly used to aid the various tasks around the world. The popularization of this equipment associated with the advancement of technology, particularly the miniaturization of processors, extend their functionalitys. In agricultural applications, these devices allow monitoring of production by capturing aerial images, for which are processed and identified areas of interest through specific software. The research proposes a low-cost solution capable of processing aerial images obtained by non-metric digital cameras coupled to UAV to identify gaps in plantations or estimate levels of environmental degradation, which can be deployed in small computers and low power consumption. Embedded systems coupled in UAV allow perform processing in real time, which contributes to a preventive diagnosis, reduces the response time and can avoid damages in the crop. The algorithm used is based on watershed, while the second algorithm uses classification techniques based on the 1-Nearest Neighbor (1-NN). Are used the embedded systems DE2i-150 and Intel Edison, both x86 architecture, and Raspberry Pi 2 of ARM architecture. Moreover, the technique 1-NN showed higher tolerance to lighting problems, however, require more processing power compared to the algorithm based on watershed. The results show that the proposed system is an efficient and relatively low-cost solution compared to traditional means of monitoring and can be coupled in a UAV to perform the processing during the flight.