Evaluating object detection models in deployment is challenging because ground-truth annotations are rarely available. We introduce the Cumulative Consensus Score (CCS), a label-free metric that enables continuous monitoring and comparison of detectors in real-world settings. CCS applies test-time data augmentation to each image, collects predicted bounding boxes across augmented views, and computes overlaps using Intersection over Union. Maximum overlaps are normalized and averaged across augmentation pairs, yielding a measure of spatial consistency that serves as a proxy for reliability without annotations. In controlled experiments on Open Images and KITTI, CCS achieved over 90% congruence with F1-score, Probabilistic Detection Quality, and Optimal Correction Cost. The method is model-agnostic, working across single-stage and two-stage detectors, and operates at the case level to highlight under-performing scenarios. Altogether, CCS provides a robust foundation for DevOps-style monitoring of object detectors.

翻译：在部署环境中评估目标检测模型具有挑战性，因为真实标注数据通常难以获取。本文提出累积共识分数，这是一种无需标注的评估指标，能够在真实场景下实现对检测器的持续监控与比较。CCS对每幅图像应用测试时数据增强，收集各增强视图的预测边界框，并利用交并比计算重叠度。最大重叠度经过归一化处理，并在增强图像对间取平均值，从而得到空间一致性度量，该度量可在缺乏标注的情况下作为可靠性的代理指标。在Open Images和KITTI数据集上的对照实验中，CCS与F1分数、概率检测质量及最优校正成本的吻合度超过90%。该方法具有模型无关性，适用于单阶段和两阶段检测器，并能在案例层面运行以突显性能不足的场景。总体而言，CCS为DevOps风格的目标检测器监控提供了稳健的基础框架。