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    Spatial-resolution independent object detection framework for aerial imagery

    Autor: 
    Samanta, Sidharth
    ;
    Panda, Mrutyunjaya
    ;
    Ramasubbareddy, Somula
    ;
    Sankar, S.
    ;
    Burgos, Daniel
    Fecha: 
    2021
    Palabra clave: 
    computer vision; convolutional neural network; deep learning; faster RCNN; multispectral images; object detection; remote sensing; sliding box strategy; Scopus; WOS(2)
    Revista / editorial: 
    Computers, materials and continua
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/11660
    DOI: 
    https://doi.org/10.32604/cmc.2021.014406
    Dirección web: 
    https://www.techscience.com/cmc/v68n2/42141
    Open Access
    Resumen:
    Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) object detection framework to make it independent of the image's spatial resolution and size. The sliding box strategy is used in the proposed model to segment the image while detecting. The proposed framework outperforms the state-of-the-art Faster RCNN model while processing images with significantly different spatial resolution values. The SRCNN is also capable of detecting objects in images of any size.
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