🤖 AI Summary
This study addresses the pervasive issues of semantic mislabeling and bounding box localization errors in object detection datasets by systematically evaluating, for the first time, the effectiveness of training-free feature-space methods for annotation error detection. Leveraging multiple pretrained embedding models, the approach is rigorously tested on both synthetic noise—including symmetric, asymmetric, and localization-type perturbations—and real-world annotation errors in the VOC2012 and KITTI datasets. Experimental results demonstrate that feature-space methods are highly effective at identifying semantic mislabels but exhibit limited capability in detecting localization inaccuracies. To facilitate future research, the authors publicly release all code and a curated set of verified erroneous annotations, establishing a valuable benchmark for the community.
📝 Abstract
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