Annotation errors are widespread in computer vision datasets and can significantly degrade the performance of systems trained on them, particularly in complex tasks such as object detection. Several approaches exist to identify annotation errors, including training-free feature-space methods which provide a fast and interpretable way to analyze annotations. However, the behavior on object detection annotations, which include semantic and spatial information, remains largely unexplored. In this work we analyze the applicability of feature-space-based approaches for detecting annotation errors in object detection datasets. By adapting an existing feature-space method, we show that such approaches reliably expose semantic mislabel, while positional errors remain difficult to detect. We evaluate this behavior across multiple pretrained embedding models, synthetic noise types (symmetric, asymmetric, and positional), and real-world annotation errors using VOC2012 and KITTI. All code and real-world corruptions are publicly available at the following repository: https://github.com/ ChristianSieberichs/BoundingBox\_corruption\_detection

翻译：标注错误在计算机视觉数据集中普遍存在，且会严重降低基于这些数据集训练的系统性能，尤其是在目标检测等复杂任务中。目前已有多种方法可用于识别标注错误，其中无训练的特征空间方法因能快速且可解释地分析标注而备受关注。然而，这些方法在目标检测标注（包含语义和空间信息）上的表现仍缺乏系统研究。本文分析了基于特征空间的方法在检测目标检测数据集中标注错误的适用性。通过改进现有特征空间方法，我们证明这类方法能可靠地暴露语义标注错误，但位置标注错误仍难以检测。我们使用VOC2012和KITTI数据集，在多个预训练嵌入模型、合成噪声类型（对称噪声、非对称噪声和位置噪声）及真实标注错误场景下评估了这一性能。所有代码及真实标注错误数据均开源在以下仓库：https://github.com/ChristianSieberichs/BoundingBox_corruption_detection