Labeling datasets for supervised object detection is a dull and time-consuming task. Errors can be easily introduced during annotation and overlooked during review, yielding inaccurate benchmarks and performance degradation of deep neural networks trained on noisy labels. In this work, we for the first time introduce a benchmark for label error detection methods on object detection datasets as well as a label error detection method and a number of baselines. We simulate four different types of randomly introduced label errors on train and test sets of well-labeled object detection datasets. For our label error detection method we assume a two-stage object detector to be given and consider the sum of both stages' classification and regression losses. The losses are computed with respect to the predictions and the noisy labels including simulated label errors, aiming at detecting the latter. We compare our method to three baselines: a naive one without deep learning, the object detector's score and the entropy of the classification softmax distribution. We outperform all baselines and demonstrate that among the considered methods, ours is the only one that detects label errors of all four types efficiently. Furthermore, we detect real label errors a) on commonly used test datasets in object detection and b) on a proprietary dataset. In both cases we achieve low false positives rates, i.e., we detect label errors with a precision for a) of up to 71.5% and for b) with 97%.

翻译：为目标检测数据集进行标注是一项枯燥且耗时的任务。在标注过程中容易引入错误，并在审核时被忽略，从而导致基准评估不准确，并使基于噪声标签训练的深度神经网络性能下降。本文首次在目标检测数据集上引入了标签错误检测方法的基准，同时提出了一种标签错误检测方法及若干基线模型。我们在高质量标注的目标检测数据集的训练集和测试集上模拟了四种随机引入的标签错误类型。对于所提出的标签错误检测方法，我们假设存在一个两阶段目标检测器，并考虑其两阶段分类与回归损失之和。该损失基于预测结果和包含模拟标签错误的噪声标签计算，旨在检测后者。我们将所提方法与三种基线模型进行了比较：一种不含深度学习的朴素方法、目标检测器的得分以及分类softmax分布的熵。我们的方法在所有基线模型中表现优异，并证明在所考虑的方法中，仅有我们的方法能高效检测全部四种类型的标签错误。此外，我们在（a）目标检测中常用的测试数据集和（b）专有数据集上检测了真实标签错误。在这两种情况下，我们均实现了较低的假阳性率，即在（a）中检测标签错误的准确率高达71.5%，在（b）中达到97%。