  In the recent past, algorithms based on Convolutional Neural Networks (CNNs)
have achieved significant milestones in object recognition. With large examples
of each object class, standard datasets train well for inter-class variability.
However, gathering sufficient data to train for a particular instance of an
object within a class is impractical. Furthermore, quantitatively assessing the
imaging conditions for each image in a given dataset is not feasible. By
generating sufficient images with known imaging conditions, we study to what
extent CNNs can cope with hard imaging conditions for instance-level
recognition in an active learning regime.
  Leveraging powerful rendering techniques to achieve instance-level detection,
we present results of training three state-of-the-art object detection
algorithms namely, Fast R-CNN, Faster R-CNN and YOLO9000, for hard imaging
conditions imposed into the scene by rendering. Our extensive experiments
produce a mean Average Precision score of 0.92 on synthetic images and 0.83 on
real images using the best performing Faster R-CNN. We show for the first time
how well detection algorithms based on deep architectures fare for each hard
imaging condition studied.
