Combating noisy labels in object detection datasets

TL;DR

This paper introduces CLOD, an algorithm that assesses and corrects label errors in object detection datasets, significantly improving model accuracy by cleaning dataset annotations without changing model architectures.

Contribution

The paper presents a novel Confident Learning for Object Detection (CLOD) algorithm that identifies and corrects label errors, enhancing dataset quality and model performance.

Findings

CLOD detects nearly 80% of artificially disturbed bounding boxes.

Dataset cleaning with CLOD improves mAP scores by 16% to 46%.

The method does not require modifications to existing network architectures.

Abstract

The quality of training datasets for deep neural networks is a key factor contributing to the accuracy of resulting models. This effect is amplified in difficult tasks such as object detection. Dealing with errors in datasets is often limited to accepting that some fraction of examples are incorrect, estimating their confidence, and either assigning appropriate weights or ignoring uncertain ones during training. In this work, we propose a different approach. We introduce the Confident Learning for Object Detection (CLOD) algorithm for assessing the quality of each label in object detection datasets, identifying missing, spurious, mislabeled, and mislocated bounding boxes and suggesting corrections. By focusing on finding incorrect examples in the training datasets, we can eliminate them at the root. Suspicious bounding boxes can be reviewed to improve the quality of the dataset, leading to better models without further complicating their already complex architectures. The proposed method is able to point out nearly 80% of artificially disturbed bounding boxes with a false positive rate below 0.1. Cleaning the datasets by applying the most confident automatic suggestions improved mAP scores by 16% to 46%, depending on the dataset, without any modifications to the network architectures. This approach shows promising potential in rectifying state-of-the-art object detection datasets.