  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 https://ghassanalregib.com/cure-or/.
