AdaTent++: reliability-guided hybrid test-time adaptation for traffic sign detection under distribution shift

Abstract

Test-time adaptation (TTA) can improve detector robustness under distribution shift, but indiscriminate online updating may amplify errors from unreliable frames. We present AdaTent++, a reliability-guided hybrid TTA framework for traffic sign detection. For each unlabeled frame, the framework combines predictive entropy and cross-view agreement to select among a smoothed batched normalization (BN)-statistics update, a guarded BN-affine gradient update, and no adaptation. Exponential moving average (EMA) threshold tracking, prototype regularization, gradient control, and state restoration are used as supporting safeguards rather than claimed as independent novelties. On one public traffic sign dataset evaluated under 12 corrupted domains, AdaTent++ achieves a mean average precision (mAP50) of 68.87%, compared with 68.03% for the strongest baseline, T3A, corresponding to an improvement of 0.84 percentage points. It ranks first in three of the twelve domains and is less effective under several structure-destructive corruptions. The results indicate a favorable average trade-off in the studied setting, while cross-dataset, cross-architecture, embedded-device, and long-horizon validation remain open.