不训练即可检测目标检测数据集中的标注错误，对语义错误有效，位置错误难识别。
Analyzing Training-Free Corruption Detection for Object Detection Datasets
- 基于特征空间方法，无需训练即可分析标注质量。
- 语义误标检测准确率高，位置误差检测效果差。
- 适用于真实数据和合成噪声，适合数据清洗与质检人员。
计算机视觉数据集中普遍存在标注错误，尤其在目标检测等复杂任务中会显著降低模型性能。现有训练无关的特征空间方法可快速、可解释地分析标注问题，但其在包含语义与空间信息的目标检测标注上的表现仍不明确。本文通过改进已有特征空间方法，验证了该类方法能可靠识别语义误标，而对位置误差的检测能力有限。我们在VOC2012和KITTI数据集上评估了多种预训练嵌入模型、三类合成噪声（对称、非对称、位置型）及真实标注错误，结果一致。所有代码与真实污染数据已公开于https://github.com/ChristianSieberichs/BoundingBox_corruption_detection。
原文摘要 · Abstract (English)
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
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