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Smart Solution to Detect Images in Limited Visibility Conditions Based Convolutional Neural Networks

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1287))

Abstract

Decrease in visibility causes many difficulties in vision, tracking. Current classic object detection techniques do not give satisfying results in less visibility. It is essential to detect and recognize the objects under such conditions and devise a better object detection mechanism. The paper proposes a solution to this problem by using a multi step approach that uses Saliency techniques and modern object detection algorithms to obtain the desired results. The distorted image is enhanced via a deep neural network for visibility enhancement. The image frame of a better quality undergoes saliency techniques so that less visible objects are visible. Faster Region-based Convolutional Neural Network (R-CNN) then runs on the saliency output to yield bounding boxes for all the objects. The coordinates of the bounding boxes are then applied on the original image thus detecting all the objects in a distorted image with less visibility.

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Correspondence to Ha Huy Cuong Nguyen .

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Nguyen, H.H.C., Nguyen, D.H., Nguyen, V.L., Nguyen, T.T. (2020). Smart Solution to Detect Images in Limited Visibility Conditions Based Convolutional Neural Networks. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_52

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  • DOI: https://doi.org/10.1007/978-3-030-63119-2_52

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63118-5

  • Online ISBN: 978-3-030-63119-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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