# Training Object Detectors With Noisy Data

arXiv: https://arxiv.org/abs/1905.07202 · HTML: https://mlsanity.com/paper/1905.07202/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2019.

## Built on by (most cited first, 19 of 19)

- [Towards Robust Adaptive Object Detection under Noisy Annotations](https://mlsanity.com/paper/2204.02620/) (2022)
- [Robust Object Detection With Inaccurate Bounding Boxes](https://mlsanity.com/paper/2207.09697/) (2022)
- [Identifying Label Errors in Object Detection Datasets by Loss Inspection](https://mlsanity.com/paper/2303.06999/) (2023)
- [SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Mining](https://mlsanity.com/paper/2201.04620/) (2022)
- [Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes](https://mlsanity.com/paper/2307.12101/) (2023)
- [Combating noisy labels in object detection datasets](https://mlsanity.com/paper/2211.13993/) (2022)
- [Narrowing the Gap: Improved Detector Training with Noisy Location Annotations](https://mlsanity.com/paper/2206.05708/) (2022)
- [Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotations](https://mlsanity.com/paper/2409.09412/) (2024)
- [Robust Tiny Object Detection in Aerial Images amidst Label Noise](https://mlsanity.com/paper/2401.08056/) (2024)
- [Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes](https://mlsanity.com/paper/2308.12017/) (2023)
- [Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detection](https://mlsanity.com/paper/2312.13822/) (2023)
- [NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detection](https://mlsanity.com/paper/2409.09424/) (2024)
- [Dynamic Supervisor for Cross-dataset Object Detection](https://mlsanity.com/paper/2204.00183/) (2022)
- [Mitigating the Impact of Prominent Position Shift in Drone-based RGBT Object Detection](https://mlsanity.com/paper/2502.09311/) (2025)
- [Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Dataset](https://mlsanity.com/paper/2508.06537/) (2025)
- [Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Night](https://mlsanity.com/paper/2204.11535/) (2022)
- [Automatic Image Annotation for Mapped Features Detection](https://mlsanity.com/paper/2412.10438/) (2024)
- [FMG-Det: Foundation Model Guided Robust Object Detection](https://mlsanity.com/paper/2505.23726/) (2025)
- [Noise-Robust Tiny Object Localization with Flows](https://mlsanity.com/paper/2601.00617/) (2026)

## Abstract (arXiv)

The availability of a large quantity of labelled training data is crucial for the training of modern object detectors. Hand labelling training data is time consuming and expensive while automatic labelling methods inevitably add unwanted noise to the labels. We examine the effect of different types of label noise on the performance of an object detector. We then show how co-teaching, a method developed for handling noisy labels and previously demonstrated on a classification problem, can be improved to mitigate the effects of label noise in an object detection setting. We illustrate our results using simulated noise on the KITTI dataset and on a vehicle detection task using automatically labelled data.