Cumulative Consensus Score: Label-Free and Model-Agnostic Evaluation of Object Detectors in Deployment

• URL: http://arxiv.org/abs/2509.12871v1
• Date: Tue, 16 Sep 2025 09:24:37 GMT
• Title: Cumulative Consensus Score: Label-Free and Model-Agnostic Evaluation of Object Detectors in Deployment
• Authors: Avinaash Manoharan, Xiangyu Yin, Domenik Helm, Chih-Hong Cheng,
• Abstract summary: evaluating object detection models in deployment is challenging because ground-truth annotations are rarely available.<n>We introduce the Cumulative Consensus Score (CCS), a label-free metric that enables continuous monitoring and comparison of detectors in real-world settings.
• Score: 3.6178660238507843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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