  Explaining predictions of deep neural networks (DNNs) is an important and
nontrivial task. In this paper, we propose a practical approach to interpret
decisions made by a DNN object detector that has fidelity comparable to
state-of-the-art methods and sufficient computational efficiency to process
large datasets. Our method relies on recent theory and approximates Shapley
feature importance values. We qualitatively and quantitatively show that the
proposed explanation method can be used to find image features which cause
failures in DNN object detection. The developed software tool combined into the
"Explain to Fix" (E2X) framework has a factor of 10 higher computational
efficiency than prior methods and can be used for cluster processing using
graphics processing units (GPUs). Lastly, we propose a potential extension of
the E2X framework where the discovered missing features can be added into
training dataset to overcome failures after model retraining.
