Towards Trustworthy Object Detection: Adaptive Boundary Optimization with Joint Classification and Localization Risk Control

Quantifying image-level uncertainty in multi-object detection is paramount for safety-critical applications, such as autonomous driving. While Conformal Risk Control (CRC) provides marginal coverage guarantees, achieving rigorous risk control for individual images remains a significant challenge. To address this problem, we propose a novel approach that learns image-adaptive boundary extension thresholds to ensure consistent detection reliability. Specifically, we introduce the Conditional Adaptive Boundary Extension (CABE) framework, which leverages a differentiable surrogate loss based on soft Intersection-over-Ground-Truth (IoG) to directly optimize for improved conditional coverage. Experiments across three diverse safety-critical domains demonstrate that CABE not only achieves superior conditional coverage compared to standard methods but also significantly reduces the magnitude of boundary expansion. Our approach offers a robust pathway for reliable and interpretable uncertainty estimation in object detection, providing vital conditional guarantees required for high-stakes applications.