Spatial Sensitive Grad-CAM++: Towards High-Quality Visual Explanations for Object Detectors via Weighted Combination of Gradient Maps

Visual explanation for object detectors is crucial for ensuring model reliability and promoting their application across a variety of domains. However, existing visual explanation methods often produce low-quality heat maps that highlight only parts of important regions for detected instances. This limitation reduces the interpretability of object detectors and hinders effective model analysis. To address this issue, we propose Spatial Sensitive Grad-CAM++, a visual explanation method for object detectors designed to enhance the quality of heat maps. The proposed method introduces a weighted combination of gradient maps into the heat map computation, taking into account the architecture of object detectors. This approach enables a more accurate representation of each CNN channel’s contribution to the final heat map, resulting in high-quality heat maps that better identify important regions. In quantitative evaluations using the deletion and insertion metrics, we confirm that the proposed method outperforms existing methods by approximately 30% and 8%, respectively. In qualitative evaluations, we further demonstrate the superiority of the proposed method. These results suggest that the proposed method provides more faithful explanations, allowing for more accurate and reliable model analysis.