发表机构
University of Tsukuba（筑波大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有指标无法表征N版本目标检测系统可靠性的问题，提出Cov_OD与Cer_OD两个无需依赖投票策略的指标，通过自动驾驶案例验证其可用于版本选择、投票策略选择及系统增量构建。
AI 中文摘要
N版本目标检测（OD）是一种通过使用多个模型或输入帧使检测结果多样化，并通过聚合单个结果减少检测误差的方法。多个检测结果之间的多样性和一致性是表征N版本OD系统可能配置可靠性的关键信息。然而，现有的性能指标如mAP和准确率仅基于聚合后的最终结果定义，无法捕获这些因素。为克服此局限，我们提出了两个专门为N版本OD定义的可靠性指标，即OD中误差的覆盖率（Cov_OD）和OD中准确预测的确定性（Cer_OD），它们可从单个检测结果计算得出，无需依赖投票策略。我们通过自动驾驶模拟器中车辆的N版本OD案例研究，实证展示了所提指标的独特特征。结果表明，所提指标可用于：1）选择具有互补误差特征的版本组合；2）基于多样性和一致性概况选择有效的投票策略；3）通过成对双版本分析指导N版本OD系统的增量构建。这些结果凸显了可指导可靠N版本OD应用设计的指标的重要性。
英文摘要
N-version object detection (OD) is an approach to diversifying detection results using multiple models or input frames and reducing detection errors by aggregating individual results. Diversity and consistency across multiple detection results are critical information for characterizing the reliability of possible configurations of N-version OD systems. However, existing performance metrics such as mAP and Accuracy fail to capture these factors, as they are defined solely on the final outcome after aggregation. To overcome this limitation, we propose two reliability metrics particularly defined for N-version OD, namely coverage of errors in OD (Cov_OD) and certainty of accurate prediction in OD (Cer_OD), which can be computed from individual detection results without relying on voting strategies. We empirically demonstrate the unique features of the proposed metrics through a case study of N-version OD for a vehicle in an autonomous-driving simulator. We show that the proposed metrics can be used for 1) selecting version combinations with complementary error characteristics, 2) choosing an effective voting strategy based on diversity and consistency profiles, and 3) guiding incremental construction of N-version OD systems via pairwise two-version analysis. These results highlight the importance of the metrics that can guide the design of reliable N-version OD applications.
Comments12pages, 6 figures, accepted to ISSRE 2026
