The UG$^{2+}$ challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual recognition in various scenarios. In its second track, we focus on object or face detection in poor visibility enhancements caused by bad weathers (haze, rain) and low light conditions. While existing enhancement methods are empirically expected to help the high-level end task, that is observed to not always be the case in practice. 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 annotate objects/faces annotated. 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. We expect a large participation from the broad research community to address these challenges together.

UG2+挑战赛2019年IEEE CVPR旨在讨论和探索低级别视觉技术如何在各种场景中有助于高级别自动视觉识别。其第二个跟踪，我们专注于在恶劣天气（雾，雨）和低光条件下导致的目标或人脸检测的可见度增强。为了提供更彻底的检查和公平比较，我们在真实的雾，雨和低光条件下引入了三个基准集合，分别注释了对象/人脸。通过级联现有增强和检测模型报告基线结果，表明我们的新数据具有极大的挑战性以及进一步技术创新的巨大空间。

UG$^{2+}$ Track 2：在恶劣环境下评估和推进图像理解的集体基准努力