# Identifying Label Errors in Object Detection Datasets by Loss Inspection

arXiv: https://arxiv.org/abs/2303.06999 · HTML: https://mlsanity.com/paper/2303.06999/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 52 references that are in the map.
- First posted on arXiv 13 Mar 2023.

## Builds on (most cited first, 30 of 30)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://mlsanity.com/paper/1712.05055/) (2017)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Training Deep Neural Networks on Noisy Labels with Bootstrapping](https://mlsanity.com/paper/1412.6596/) (2014)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise](https://mlsanity.com/paper/1802.05300/) (2018)
- [Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels](https://mlsanity.com/paper/1905.05040/) (2019)
- [Dropout Sampling for Robust Object Detection in Open-Set Conditions](https://mlsanity.com/paper/1710.06677/) (2017)
- [A Survey of Deep Learning Applications to Autonomous Vehicle Control](https://mlsanity.com/paper/1912.10773/) (2019)
- [Towards Adversarially Robust Object Detection](https://mlsanity.com/paper/1907.10310/) (2019)
- [Active Learning for Deep Object Detection](https://mlsanity.com/paper/1809.09875/) (2018)
- [Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection](https://mlsanity.com/paper/1811.08661/) (2018)
- [Learning from Noisy Anchors for One-stage Object Detection](https://mlsanity.com/paper/1912.05086/) (2019)
- [Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection](https://mlsanity.com/paper/2106.11921/) (2021)
- [Soft Sampling for Robust Object Detection](https://mlsanity.com/paper/1806.06986/) (2018)
- [An Adaptive Supervision Framework for Active Learning in Object Detection](https://mlsanity.com/paper/1908.02454/) (2019)
- [Automated Detection of Label Errors in Semantic Segmentation Datasets via Deep Learning and Uncertainty Quantification](https://mlsanity.com/paper/2207.06104/) (2022)
- [Training Object Detectors With Noisy Data](https://mlsanity.com/paper/1905.07202/) (2019)
- [Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection](https://mlsanity.com/paper/2012.12195/) (2020)
- [Identifying Incorrect Annotations in Multi-Label Classification Data](https://mlsanity.com/paper/2211.13895/) (2022)
- [Effect of Annotation Errors on Drone Detection with YOLOv3](https://mlsanity.com/paper/2004.01059/) (2020)
- [Finding Label and Model Errors in Perception Data With Learned Observation Assertions](https://mlsanity.com/paper/2201.05797/) (2022)
- [Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection](https://mlsanity.com/paper/2212.10836/) (2022)

## Built on by (most cited first, 16 of 16)

- [RePOPE: Impact of Annotation Errors on the POPE Benchmark](https://mlsanity.com/paper/2504.15707/) (2025)
- [Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models](https://mlsanity.com/paper/2406.17115/) (2024)
- [LEMoN: Label Error Detection using Multimodal Neighbors](https://mlsanity.com/paper/2407.18941/) (2024)
- [Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations](https://mlsanity.com/paper/2409.09412/) (2024)
- [Sample Selection via Contrastive Fragmentation for Noisy Label Regression](https://mlsanity.com/paper/2502.17771/) (2025)
- [Deep Active Learning with Noisy Oracle in Object Detection](https://mlsanity.com/paper/2310.00372/) (2023)
- [LED Benchmark: Diagnosing Structural Layout Errors for Document Layout Analysis](https://mlsanity.com/paper/2507.23295/) (2025)
- [GRAID: Enhancing Spatial Reasoning of VLMs Through High-Fidelity Data Generation](https://mlsanity.com/paper/2510.22118/) (2025)
- [Label-Free Model Failure Detection for Lidar-based Point Cloud Segmentation](https://mlsanity.com/paper/2407.14306/) (2024)
- [Pseudo-Labeling Driven Refinement of Benchmark Object Detection Datasets via Analysis of Learning Patterns](https://mlsanity.com/paper/2506.00997/) (2025)
- [LED: A Benchmark for Evaluating Layout Error Detection in Document Analysis](https://mlsanity.com/paper/2603.17265/) (2026)
- [From Label Error Detection to Correction: A Modular Framework and Benchmark for Object Detection Datasets](https://mlsanity.com/paper/2508.06556/) (2025)
- [Learning to Detect Label Errors by Making Them: A Method for Segmentation and Object Detection Datasets](https://mlsanity.com/paper/2508.17930/) (2025)
- [SELECT: Detecting Label Errors in Real-world Scene Text Data](https://mlsanity.com/paper/2512.14050/) (2025)
- [Analyzing Training-Free Corruption Detection for Object Detection Datasets](https://mlsanity.com/paper/2606.10666/) (2026)
- [SULAND v2: A Refined RGB Dataset and Deep Learning Object Detection Benchmark for UAV/UGV-Based SUrface LANDmine Detection Under Domain Shift](https://mlsanity.com/paper/2607.28996/) (2026)

## Abstract (arXiv)

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%.