  We propose D-RISE, a method for generating visual explanations for the
predictions of object detectors. Utilizing the proposed similarity metric that
accounts for both localization and categorization aspects of object detection
allows our method to produce saliency maps that show image areas that most
affect the prediction. D-RISE can be considered "black-box" in the software
testing sense, as it only needs access to the inputs and outputs of an object
detector. Compared to gradient-based methods, D-RISE is more general and
agnostic to the particular type of object detector being tested, and does not
need knowledge of the inner workings of the model. We show that D-RISE can be
easily applied to different object detectors including one-stage detectors such
as YOLOv3 and two-stage detectors such as Faster-RCNN. We present a detailed
analysis of the generated visual explanations to highlight the utilization of
context and possible biases learned by object detectors.
