  Recent advances in Artificial Intelligence (AI) technology have promoted
their use in almost every field. The growing complexity of deep neural networks
(DNNs) makes it increasingly difficult and important to explain the inner
workings and decisions of the network. However, most current techniques for
explaining DNNs focus mainly on interpreting classification tasks. This paper
proposes a method to explain the decision for any object detection model called
D-CLOSE. To closely track the model's behavior, we used multiple levels of
segmentation on the image and a process to combine them. We performed tests on
the MS-COCO dataset with the YOLOX model, which shows that our method
outperforms D-RISE and can give a better quality and less noise explanation.
