@Article{VelameBinsMura:2020:CaBaIm,
               author = "Velame, Vict{\'o}ria Maria Gomes and Bins, Leonardo Sant'Anna and 
                         Mura, Jos{\'e} Cl{\'a}udio",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Captive balloon image object detection system using deep 
                         learning",
              journal = "Journal of Applied Remote Sensing",
                 year = "2020",
               volume = "14",
               number = "3",
                pages = "e036517",
                month = "Sept.",
             keywords = "deep learning, object detection, remote sensing, captive 
                         balloon.",
             abstract = "The surveillance of large areas to ensure local security requires 
                         remote sensors with high temporal and spatial resolution. Captive 
                         balloons with infrared and visible sensors, like ALTAVE captive 
                         balloon system, can perform a long-term day-night surveillance and 
                         provide security of large areas by monitoring people and vehicles, 
                         but it is an exhaustive task for a human. In order to provide a 
                         more efficient and less arduous monitoring, a deep learning model 
                         was trained to detect people and vehicles in images from captive 
                         balloons infrared and visible sensors. Two databases containing 
                         about 700 images each, one for each sensor, were manually built. 
                         Two networks were fine-tuned from a pretrained faster region-based 
                         convolution neural network (R-CNN). The network reached accuracies 
                         of 87.1% for the infrared network and 86.1% for the visible one. 
                         Both networks were able to satisfactorily detect multiple objects 
                         in an image with a variety of angles, positions, types (for 
                         vehicles), scales, and even with some noise and overlap. Thus a 
                         faster R-CNN pretrained only in common RGB (red, green, and blue) 
                         images can be fine-tuned to work satisfactorily on visible remote 
                         sensing (RS) images and even on the infrared RS images.",
                  doi = "10.1117/1.JRS.14.036517",
                  url = "http://dx.doi.org/10.1117/1.JRS.14.036517",
                 issn = "1931-3195",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}