  To address the challenges of providing quick and plausible explanations in
Explainable AI (XAI) for object detection models, we introduce the Gaussian
Class Activation Mapping Explainer (G-CAME). Our method efficiently generates
concise saliency maps by utilizing activation maps from selected layers and
applying a Gaussian kernel to emphasize critical image regions for the
predicted object. Compared with other Region-based approaches, G-CAME
significantly reduces explanation time to 0.5 seconds without compromising the
quality. Our evaluation of G-CAME, using Faster-RCNN and YOLOX on the MS-COCO
2017 dataset, demonstrates its ability to offer highly plausible and faithful
explanations, especially in reducing the bias on tiny object detection.
