  State-of-the-art object detectors are treated as black boxes due to their
highly non-linear internal computations. Even with unprecedented advancements
in detector performance, the inability to explain how their outputs are
generated limits their use in safety-critical applications. Previous work fails
to produce explanations for both bounding box and classification decisions, and
generally make individual explanations for various detectors. In this paper, we
propose an open-source Detector Explanation Toolkit (DExT) which implements the
proposed approach to generate a holistic explanation for all detector decisions
using certain gradient-based explanation methods. We suggests various
multi-object visualization methods to merge the explanations of multiple
objects detected in an image as well as the corresponding detections in a
single image. The quantitative evaluation show that the Single Shot MultiBox
Detector (SSD) is more faithfully explained compared to other detectors
regardless of the explanation methods. Both quantitative and human-centric
evaluations identify that SmoothGrad with Guided Backpropagation (GBP) provides
more trustworthy explanations among selected methods across all detectors. We
expect that DExT will motivate practitioners to evaluate object detectors from
the interpretability perspective by explaining both bounding box and
classification decisions.
