Identifying Label Errors in Object Detection Datasets by Loss Inspection

• URL: http://arxiv.org/abs/2303.06999v3
• Date: Tue, 19 Dec 2023 07:30:25 GMT
• Title: Identifying Label Errors in Object Detection Datasets by Loss Inspection
• Authors: Marius Schubert, Tobias Riedlinger, Karsten Kahl, Daniel Kr\"oll, Sebastian Schoenen, Sini\v{s}a \v{S}egvi\'c, Matthias Rottmann
• Abstract summary: We introduce a benchmark for label error detection methods on object detection datasets. We simulate four different types of randomly introduced label errors on train and test sets of well-labeled object detection datasets.
• Score: 4.442111891959355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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