  In this paper, we propose in our novel generative framework the use of
Generative Adversarial Networks (GANs) to generate features that provide
robustness for object detection on reduced quality images. The proposed
GAN-based Detection of Objects (GAN-DO) framework is not restricted to any
particular architecture and can be generalized to several deep neural network
(DNN) based architectures. The resulting deep neural network maintains the
exact architecture as the selected baseline model without adding to the model
parameter complexity or inference speed. We first evaluate the effect of image
quality not only on the object classification but also on the object bounding
box regression. We then test the models resulting from our proposed GAN-DO
framework, using two state-of-the-art object detection architectures as the
baseline models. We also evaluate the effect of the number of re-trained
parameters in the generator of GAN-DO on the accuracy of the final trained
model. Performance results provided using GAN-DO on object detection datasets
establish an improved robustness to varying image quality and a higher mAP
compared to the existing approaches.
