In this paper, we introduce a large-scale, controlled, and multi-platform
object recognition dataset denoted as Challenging Unreal and Real Environments
for Object Recognition (CURE-OR). In this dataset, there are 1,000,000 images
of 100 objects with varying size, color, and texture that are positioned in
five different orientations and captured using five devices including a webcam,
a DSLR, and three smartphone cameras in real-world (real) and studio (unreal)
environments. The controlled challenging conditions include underexposure,
overexposure, blur, contrast, dirty lens, image noise, resizing, and loss of
color information. We utilize CURE-OR dataset to test recognition APIs-Amazon
Rekognition and Microsoft Azure Computer Vision- and show that their
performance significantly degrades under challenging conditions. Moreover, we
investigate the relationship between object recognition and image quality and
show that objective quality algorithms can estimate recognition performance
under certain photometric challenging conditions. The dataset is publicly
available at this https URL

本文介绍了一个大规模、受控制的、多平台物体识别数据集，命名为 CURE-OR。该数据集用于测试 Amazon Rekognition 和 Microsoft Azure Computer Vision 的性能，并显示它们在具有挑战性的条件下的性能显着降低。此外，作者还研究了物体识别和图像质量之间的关系，并表明，客观质量算法可以在某些光度挑战性条件下估计识别性能。