We propose Differences in Detection (DnD), an intuitive method to compare two object detection models. Based on the same matching algorithm, it complements the standard metrics of mean Average Precision ($mAP$) and TIDE error analysis with the ability to compare two models directly. More specifically, we calculate the intersection of ground truth labels that are recognized by both models, followed by the corresponding difference sets and the complement set of ground truth labels that are missed by both models. The resulting comparison is more direct and intuitive than a comparison of independent summary statistics. It reveals individual and shared mistakes and becomes particularly interesting when combined with error types. In this case, the differences in detection errors can be analyzed naturally in a standard confusion matrix. While valuable in itself, we believe that one of the best applications of DnD is to guide explainability methods such as ODAM towards metric-relevant examples, grounded in structured subsets. The code for our method is available here: https://github.com/JohannesTheo/differences-in-detection

翻译：我们提出检测差异（Differences in Detection, DnD）方法，这是一种直观比较两个目标检测模型的框架。该方法基于相同匹配算法，在平均精度均值（$mAP$）和TIDE误差分析等标准指标基础上，新增直接比较两个模型的能力。具体而言，我们首先计算两个模型均能正确识别的真实标签交集，继而推导出对应的差异集合，以及两个模型均遗漏的真实标签补集。这种比较方式比独立统计汇总的对比更加直接直观，能够揭示模型各自及共同的错误，当与误差类型结合时尤为显著。在此情况下，检测误差的差异可以自然地通过标准混淆矩阵进行分析。尽管该方法本身具有重要价值，但我们认为DnD的最佳应用之一，是引导诸如ODAM等可解释性方法聚焦于与度量相关的样本，并建立基于结构化子集的解释框架。本方法代码可于以下地址获取：https://github.com/JohannesTheo/differences-in-detection