  The robustness of object detection algorithms plays a prominent role in
real-world applications, especially in uncontrolled environments due to
distortions during image acquisition. It has been proven that the performance
of object detection methods suffers from in-capture distortions. In this study,
we present a performance evaluation framework for the state-of-the-art object
detection methods using a dedicated dataset containing images with various
distortions at different levels of severity. Furthermore, we propose an
original strategy of image distortion generation applied to the MS-COCO dataset
that combines some local and global distortions to reach much better
performances. We have shown that training using the proposed dataset improves
the robustness of object detection by 31.5\%. Finally, we provide a custom
dataset including natural images distorted from MS-COCO to perform a more
reliable evaluation of the robustness against common distortions. The database
and the generation source codes of the different distortions are made publicly
available
