Ensuring the performance of object detection systems in dynamic environments requires not only accurate predictions but also the ability to assess the certainty of those predictions. This paper proposes an interpretable framework for monitoring the Operational Design Domain (ODD) of real-time object detectors through visual feature-based certainty evaluation. Using a dual-path architecture, the system combines a standard object detection pipeline with a parallel branch that extracts visual features and classifies predictions as Certain or Uncertain using decision rules learned via Decision Trees (DTs). A Cumulative Feature Ranking (CFR) strategy ensures robust selection of discriminative features across perturbed and real-world datasets. Extensive experiments on the Pedestrian and Wheelchair object categories demonstrate the system’s ability to detect prediction uncertainty under a variety of visual conditions. The interpretable nature of the learned rules provides transparency, while the low false alarm rate demonstrates the effectiveness of the ODD checker in supporting safe and explainable perception for indoor smart mobility applications.