  In recent years, deep neural networks have been widely used for building
high-performance Artificial Intelligence (AI) systems for computer vision
applications. Object detection is a fundamental task in computer vision, which
has been greatly progressed through developing large and intricate deep
learning models. However, the lack of transparency is a big challenge that may
not allow the widespread adoption of these models. Explainable artificial
intelligence is a field of research where methods are developed to help users
understand the behavior, decision logics, and vulnerabilities of AI systems.
Previously, few explanation methods were developed for object detection, based
on the idea of random masks. However, random masks may raise some issues
regarding the actual importance of pixels within an image. In this paper, we
design and implement a black-box explanation method named Black-box Object
Detection Explanation by Masking (BODEM) through adopting a hierarchical random
masking approach for AI-based object detection systems. We propose a
hierarchical random masking framework in which coarse-grained masks are used in
lower levels to find salient regions within an image, and fine-grained mask are
used to refine the salient regions in higher levels. Experimentations on
various object detection datasets and models showed that BODEM can be
effectively used to explain the behavior of object detectors. Moreover, our
method outperformed Detector Randomized Input Sampling for Explanation (D-RISE)
with respect to different quantitative measures of explanation effectiveness.
The experimental results demonstrate that BODEM can be an effective method for
explaining and validating object detection systems in black-box testing
scenarios.
