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dc.contributor.authorLi, Guiyuanen_US
dc.contributor.authorZong, Changfuen_US
dc.contributor.authorZhang, Dongen_US
dc.contributor.authorZhu, Tianjunen_US
dc.contributor.authorLi, Jianyingen_US
dc.identifier.citationLi, G., Zong, C., Zhang, D., Zhu, T. & Li, J. (2021). Simple global thresholding neural network for shadow detection. Sensors and Materials, 33(9), 3307-3316.
dc.description.abstractShadow detection based on vision sensors is widely used in image processing. Because of the variability of illumination and projection surface color, shadow detection based on a color image is a challenging problem. Aiming at solving the conflict between the complexity and robustness of current shadow detection algorithms, we established a new shadow detection network by combining the global thresholding method with a neural network, which realized the decoupling of the global threshold and binary fusion. Three public shadow detection datasets, large-scale shadow dataset of Stony Brook University (SBU), large-scale dataset with image shadow triplets (ISTD), and shadow detection for mobile robots features evaluation and datasets (SDMR), were utilized for its verification. Experimental results show that the performance of the proposed network approaches that of previous deep learning methods, both visually and in terms of objective indicators, but the proposed network has the advantages of a simple structure and good robustness.en_US
dc.relation.ispartofSensors and Materialsen_US
dc.rights© 2021 MYU K.K. All rights reserved. This paper was published in Sensors and Materials and is made available with permission of MYU K.K.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleSimple global thresholding neural network for shadow detectionen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionPublished versionen_US
dc.subject.keywordsShadow Detectionen_US
dc.subject.keywordsGlobal Thresholden_US
dc.description.acknowledgementThis work is supported by the Scientific Study Project for Institutes of Higher Learning of Liaoning Provincial Department of Education (JP2016018), Characteristic Innovation Project of Guangdong Provincial Department of Education (2019KTSCX201), Zhaoqing Research and Development Technology and Application of Energy Conservation and Environmental Protection Ecological Governance (2020SN004), Teaching Quality and Reform of Higher Vocational Education Project of Guangdong Province (GDJG2019463), 2021 Special Projects in Key Fields of Colleges and Universities of Guangdong Province (2021ZDZX1061), and Youth Innovative Talents Project of Guangdong Provincial Department of Education (2018KQNCX290).en_US
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