Object detection has advanced rapidly in recent years, driven by increasingly large and diverse datasets. However, label errors often compromise the quality of these datasets and affect the outcomes of training and benchmark evaluations. Although label error detection methods for object detection datasets now exist, they are typically validated only on synthetic benchmarks or via limited manual inspection. How to correct such errors systematically and at scale remains an open problem. We introduce a semi-automated framework for label error correction called Rechecked. Building on existing label error detection methods, their error proposals are reviewed with lightweight, crowd-sourced microtasks. We apply Rechecked to the class pedestrian in the KITTI dataset, for which we crowdsourced high-quality corrected annotations. We detect 18% of missing and inaccurate labels in the original ground truth. We show that current label error detection methods, when combined with our correction framework, can recover hundreds of errors with little human effort compared to annotation from scratch. However, even the best methods still miss up to 66% of the label errors, which motivates further research, now enabled by our released benchmark.

翻译：近年来，目标检测领域因日益庞大且多样化的数据集而快速发展。然而，标签错误常常损害这些数据集的质量，并影响训练与基准评估的结果。尽管目前已有针对目标检测数据集的标签错误检测方法，但它们通常仅在合成基准上或通过有限的人工检查进行验证。如何系统化、大规模地修正此类错误仍是一个悬而未决的问题。我们提出一个名为Rechecked的半自动化标签错误修正框架。该框架基于现有的标签错误检测方法，通过轻量级的众包微任务对其错误提案进行复核。我们将Rechecked应用于KITTI数据集中的行人类别，并为此众包了高质量的修正标注。我们检测出原始标注中18%的缺失与不准确标签。研究表明，当前的标签错误检测方法结合我们的修正框架，能够以远低于从头标注的人力成本恢复数百个错误。然而，即使最优方法仍会遗漏高达66%的标签错误，这激励了进一步的研究，而我们所发布的基准为此提供了支持。