Robust Road Object Detection with CAESAR: Context-Aware Explanations via Semantic Attribution and Refinement

Contemporary road object detectors face entangled challenges of opaque decision-making, occlusions, and adverse weather, yet no existing method addresses all simultaneously. We introduce CAESAR (Context-Aware Explanations via Semantic Attribution and Refinement), a model-agnostic framework that improves baseline detectors’ performance while generating faithful, human-aligned, instance-level explanations. CAESAR combines top-down contextual cues with bottom-up features, adaptively balancing global semantics and local details according to prediction uncertainty. When integrated into state-of-the-art detectors, CAESAR consistently enhances detection accuracy and robustness across unfavorable conditions on TJU-DHD-Traffic and BDD100K datasets. Its modular context component supports standalone recalculation for domain adaptation without altering the core detector. Faithfulness evaluation confirms its alignment with the model’s internal reasoning, and an ablation study demonstrates its compatibility with complementary components for optimal performance. By leveraging human attention strategies, CAESAR advances trustworthy vision systems with robust road object detection for autonomous driving and beyond.