Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study
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DOI：
10.1109/tip.2020.2981922
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发表时间：
2019-04
影响因子：
10.6
通讯作者：
Wenhan Yang;Ye Yuan;Wenqi Ren;Jiaying Liu;W. Scheirer;Zhangyang Wang;Taiheng Zhang;Qiaoyong Zhong-Qiaoyong
中科院分区：
文献类型：
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作者：
Wenhan Yang;Ye Yuan;Wenqi Ren;Jiaying Liu;W. Scheirer;Zhangyang Wang;Taiheng Zhang;Qiaoyong Zhong-Qiaoyong
根据经验，现有的增强方法有望帮助高端计算机视觉任务：然而，在实践中观察到情况并不总是如此。我们专注于在恶劣天气(雾霾、雨)和弱光条件下导致的低能见度增强情况下的目标或人脸检测。为了提供更全面的检查和公平的比较，我们引入了三个基准集，分别在真实世界的雾霾、下雨和弱光条件下收集，带有注释的对象/脸。我们在IEEE CVPR 2019推出了UG2+Challenger Track 2大赛，旨在引发对各种场景下低级视觉技术是否以及如何有利于高级自动视觉识别的全面讨论和探索。据我们所知，这是此类努力中的第一次，也是目前最大的一次。报告了通过级联现有增强和检测模型的基线结果，表明我们的新数据具有高度挑战性，以及进一步技术创新的巨大空间。由于研究界的广泛参与，我们能够分析具有代表性的团队解决方案，努力更好地确定现有思维模式的优势和局限性以及未来的方向。
Existing enhancement methods are empirically expected to help the high-level end computer vision task: however, that is observed to not always be the case in practice. We focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. To provide a more thorough examination and fair comparison, we introduce three benchmark sets collected in real-world hazy, rainy, and low-light conditions, respectively, with annotated objects/faces. We launched the UG2+ challenge Track 2 competition in IEEE CVPR 2019, aiming to evoke a comprehensive discussion and exploration about whether and how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. To our best knowledge, this is the first and currently largest effort of its kind. Baseline results by cascading existing enhancement and detection models are reported, indicating the highly challenging nature of our new data as well as the large room for further technical innovations. Thanks to a large participation from the research community, we are able to analyze representative team solutions, striving to better identify the strengths and limitations of existing mindsets as well as the future directions.