AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models

TL;DR

AutoVDC leverages vision-language models to automatically detect annotation errors in autonomous driving datasets, reducing manual review effort and improving data quality.

Contribution

This paper introduces AutoVDC, a novel framework that uses vision-language models for automated detection of annotation errors in autonomous driving datasets.

Findings

High error detection rate in KITTI and nuImages datasets.

VLM fine-tuning improves error detection accuracy.

Effective identification of intentionally injected annotation errors.

Abstract

Training of autonomous driving systems requires extensive datasets with precise annotations to attain robust performance. Human annotations suffer from imperfections, and multiple iterations are often needed to produce high-quality datasets. However, manually reviewing large datasets is laborious and expensive. In this paper, we introduce AutoVDC (Automated Vision Data Cleaning) framework and investigate the utilization of Vision-Language Models (VLMs) to automatically identify erroneous annotations in vision datasets, thereby enabling users to eliminate these errors and enhance data quality. We validate our approach using the KITTI and nuImages datasets, which contain object detection benchmarks for autonomous driving. To test the effectiveness of AutoVDC, we create dataset variants with intentionally injected erroneous annotations and observe the error detection rate of our approach. Additionally, we compare the detection rates using different VLMs and explore the impact of VLM fine-tuning on our pipeline. The results demonstrate our method's high performance in error detection and data cleaning experiments, indicating its potential to significantly improve the reliability and accuracy of large-scale production datasets in autonomous driving.