  Explainable artificial intelligence (XAI) has witnessed significant advances
in the field of object recognition, with saliency maps being used to highlight
image features relevant to the predictions of learned models. Although these
advances have made AI-based technology more interpretable to humans, several
issues have come to light. Some approaches present explanations irrelevant to
predictions, and cannot guarantee the validity of XAI (axioms). In this study,
we propose the Baseline Shapley-based Explainable Detector (BSED), which
extends the Shapley value to object detection, thereby enhancing the validity
of interpretation. The Shapley value can attribute the prediction of a learned
model to a baseline feature while satisfying the explainability axioms. The
processing cost for the BSED is within the reasonable range, while the original
Shapley value is prohibitively computationally expensive. Furthermore, BSED is
a generalizable method that can be applied to various detectors in a
model-agnostic manner, and interpret various detection targets without
fine-grained parameter tuning. These strengths can enable the practical
applicability of XAI. We present quantitative and qualitative comparisons with
existing methods to demonstrate the superior performance of our method in terms
of explanation validity. Moreover, we present some applications, such as
correcting detection based on explanations from our method.
