通过交集与差集分析两模型检测差异，直观揭示共性与独有错误。
Differences in Detection: Explainability Where it Matters
- 基于相同匹配算法计算两模型识别结果的交集与差集。
- 可识别共享错误与各自独有误检，结合错误类型生成混淆矩阵。
- 适合用于指导可解释性方法聚焦于关键误差样本。
我们提出差异检测（DnD），一种直观的两模型目标检测对比方法。在相同匹配算法基础上，该方法补充了标准的mAP和TIDE误差分析，实现两模型的直接对比。具体而言，先计算两模型均识别出的真值框交集，再分别得到各自的差异集及两者均未识别的真值框补集。相比独立统计指标，该方法更直接、直观地揭示了模型的共同错误与各自特有错误，并在结合错误类型时，能自然构建标准混淆矩阵以分析检测误差差异。虽然本身已具价值，但我们认为其最佳应用在于引导可解释性方法（如ODAM）关注与指标相关的典型错误样本，基于结构化子集进行定位。代码已开源：https://github.com/JohannesTheo/differences-in-detection。
原文摘要 · Abstract (English)
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
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