# Training Object Detectors With Noisy Data

## TL;DR

This paper investigates how label noise affects object detector training and proposes an improved co-teaching method to mitigate noise effects, demonstrating effectiveness on simulated and real-world noisy datasets.

## Contribution

It introduces an enhanced co-teaching approach tailored for object detection to handle noisy labels, extending prior classification-focused methods.

## Key findings

- Co-teaching improves detection accuracy with noisy labels.
- Simulated noise experiments validate the method's robustness.
- Real-world vehicle detection benefits from the approach.

## Abstract

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.