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Filtering by: Creator Cofield, Aaron Remove constraint Creator: Cofield, Aaron Creator El Shair, Zaid A. Remove constraint Creator: El Shair, Zaid A. Resource type Dataset Remove constraint Resource type: Dataset

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  • SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions

    work
    Creator:
    El Shair, Zaid A., Abu-raddaha, Abdalmalek , Cofield, Aaron, Alawneh, Hisham, Aladem, Mohamed, Hamzeh, Yazan, and Rawashdeh, Samir A.
    Description:
    The SID dataset was curated to support advanced research in autonomous driving systems, particularly focusing on perception under adverse weather and lighting conditions. This dataset encompasses over 178k high-resolution stereo image pairs organized into 27 sequences, reflecting a rich variety of conditions such as snow, rain, fog, and low light. It covers dynamic changes in driving scenarios and environmental backgrounds, including university campuses, residential streets, and urban settings. The dataset is designed to challenge perception algorithms with scenarios such as partially obscured camera lenses and varying visibility, promoting the development of robust computer vision models. No specialized software or scripts are necessary for accessing the image data, as the files are provided in standard PNG format. However, researchers and developers may require their image processing and computer vision toolkits to utilize the dataset effectively in their work.
    Keyword:
    Autonomous Driving, Adverse Weather, Stereo Vision, Image Dataset, Computer Vision, and Perception Algorithms
    Citation to related publication:
    El-Shair, Z.A., Abu-raddaha, A., Cofield, A., Alawneh, H., Aladem, M., Hamzeh, Y., and Rawashdeh, S.A., 2024. SID: Stereo Image Dataset for Autonomous Driving in Adverse Conditions. In 2024 IEEE National Aerospace and Electronics Conference (NAECON). IEEE. In press.
    Discipline:
    Engineering