mlsanityData to 7 Oct 2026

Training Object Detectors With Noisy Data

arXiv 1905.07202 ↗First posted 17 May 2019cs.RO cs.CVcited by 19

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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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 7; 2023: 4; 2024: 4; 2025: 3; 2026: 1.

19 papers.

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  1. 2022Towards Robust Adaptive Object Detection under Noisy Annotationscited by 20
  2. 2022Robust Object Detection With Inaccurate Bounding Boxescited by 20
  3. 2023Identifying Label Errors in Object Detection Datasets by Loss Inspectioncited by 16
  4. 2022SparseDet: Improving Sparsely Annotated Object Detection with Pseudo-positive Miningcited by 14
  5. 2023Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxescited by 9
  6. 2022Combating noisy labels in object detection datasetscited by 7
  7. 2022Narrowing the Gap: Improved Detector Training with Noisy Location Annotationscited by 6
  8. 2024Label Convergence: Defining an Upper Performance Bound in Object Recognition through Contradictory Annotationscited by 5
  9. 2024Robust Tiny Object Detection in Aerial Images amidst Label Noisecited by 4
  10. 2023Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxescited by 2
  11. 2023Universal Noise Annotation: Unveiling the Impact of Noisy annotation on Object Detectioncited by 2
  12. 2024NBBOX: Noisy Bounding Box Improves Remote Sensing Object Detectioncited by 2
7 more
  1. 2022Dynamic Supervisor for Cross-dataset Object Detectioncited by 1
  2. 2025Mitigating the Impact of Prominent Position Shift in Drone-based RGBT Object Detectioncited by 1
  3. 2025Benchmarking Deep Learning-Based Object Detection Models on Feature Deficient Astrophotography Imagery Datasetcited by 1
  4. 2022Combining Visual Saliency Methods and Sparse Keypoint Annotations to Providently Detect Vehicles at Nightcited by 0
  5. 2024Automatic Image Annotation for Mapped Features Detectioncited by 0
  6. 2025FMG-Det: Foundation Model Guided Robust Object Detectioncited by 0
  7. 2026Noise-Robust Tiny Object Localization with Flowscited by 0