Deep learning-based weather image recognition

Li Wei Kang, Ke Lin Chou, Ru Hong Fu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

25 Citations (Scopus)


Image data captured from outdoor visual devices are usually degraded by turbid media, such as haze, smoke, fog, rain, or snow. Therefore, weather conditions would usually disrupt or degrade proper functioning of vision-assisted transportation systems or ADAS (advanced driver assistance systems), as well as several other outdoor surveillance-based systems. To cope with these problems, removal of weather effects (or deweathering) from images has been important and received much attention. Hence, it is important to provide a preprocessing stage to automatically determine the weather condition for an input image, and then the corresponding proper deweathering operations (e.g., removals of haze, rain, or snow) would be correctly triggered accordingly. This paper presents a deep learning-based weather image recognition framework by considering the three most common weather conditions, including hazy, rainy, and snowy, in outdoor scenes. For an input image, our method automatically classifies the image into one of the three categories or none of them (e.g., sunny or others). Extensive experiments based on both well-known deep networks, GoogLeNet and AlexNet, are conducted on open weather image dataset to evaluate the proposed method and the feasibility has been verified.

Original languageEnglish
Title of host publicationProceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781538670361
Publication statusPublished - 2018 Jul 2
Externally publishedYes
Event4th International Symposium on Computer, Consumer and Control, IS3C 2018 - Taichung, Taiwan
Duration: 2018 Dec 62018 Dec 8

Publication series

NameProceedings - 2018 International Symposium on Computer, Consumer and Control, IS3C 2018


Conference4th International Symposium on Computer, Consumer and Control, IS3C 2018


  • AlexNet
  • Classification
  • Convolutional neural networks
  • Deep learning
  • GoogLeNet
  • Recognition
  • Weather images

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Energy Engineering and Power Technology
  • Computer Science Applications
  • Control and Optimization
  • Signal Processing


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