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.

翻译：近年来，人工智能（AI）技术的进步推动了其在几乎所有领域的应用。深度神经网络（DNNs）日益复杂，使得解释网络内部机制与决策变得愈发困难且重要。然而，当前大多数DNN解释技术主要聚焦于分类任务的解释。本文提出一种名为D-CLOSE的方法，用于解释任意目标检测模型的决策。为紧密追踪模型行为，我们采用图像的多级分割及其组合处理流程。基于MS-COCO数据集与YOLOX模型进行的测试表明，该方法性能优于D-RISE，能够提供质量更高、噪声更少的解释。