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Inception Network-Based Weather Image Classification with Pre-filtering Process

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Book cover New Trends in Computer Technologies and Applications (ICS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1013))

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Abstract

Visual data (e.g., images/videos) captured from outdoor visual devices are usually degraded by turbid media, such as haze, rain, or snow. Hence, weather conditions would usually disrupt or degrade proper functioning of vision-based applications, such as transportation systems or advanced driver assistance systems, as well as several other outdoor surveillance-based systems. To cope with these problems, removal of weather effects (or the so-called deweathering) from visual data has been critical and received much attention. Therefore, it is important to provide a preprocessing step to automatically decide the current weather condition for input visual data, and then the corresponding proper deweathering operations (e.g., removals of rain or snow) will be properly triggered accordingly. This paper presents an inception network-based weather image classification framework relying on the GoogLeNet by considering the two common weather conditions (with similar characteristics), including rain and snow, in outdoor scenes. For an input image, our method automatically classifies it into one of the two categories or none of them (e.g., sunny or others). We also evaluate the possible impact on image classification performance derived from the image preprocessing via filtering. Extensive experiments conducted on open weather image datasets with/without preprocessing are conducted to evaluate the proposed method and the feasibility has been verified.

This work was supported in part by Ministry of Science and Technology (MOST), Taiwan, under the Grant MOST 105-2628-E-224-001-MY3. This work was also financially supported by the “Artificial Intelligence Recognition Industry Service Research Center” from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan.

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Author information

Authors and Affiliations

  1. Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Li-Wei Kang

  2. Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Li-Wei Kang, Tian-Zheng Feng & Ru-Hong Fu

  3. Artificial Intelligence Recognition Industry Service Research Center, National Yunlin University of Science and Technology, Yunlin, Taiwan

    Li-Wei Kang & Tian-Zheng Feng

Authors
  1. Li-Wei Kang
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  2. Tian-Zheng Feng
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  3. Ru-Hong Fu
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Correspondence to Li-Wei Kang .

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Editors and Affiliations

  1. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chuan-Yu Chang

  2. National Yunlin University of Science and Technology, Douliu, Taiwan

    Chien-Chou Lin

  3. Southern Taiwan University of Science and Technology, Tainan, Taiwan

    Horng-Horng Lin

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Kang, LW., Feng, TZ., Fu, RH. (2019). Inception Network-Based Weather Image Classification with Pre-filtering Process. In: Chang, CY., Lin, CC., Lin, HH. (eds) New Trends in Computer Technologies and Applications. ICS 2018. Communications in Computer and Information Science, vol 1013. Springer, Singapore. https://doi.org/10.1007/978-981-13-9190-3_38

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  • DOI: https://doi.org/10.1007/978-981-13-9190-3_38

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  • Online ISBN: 978-981-13-9190-3

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