AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models

arXiv cs.RO / 5/1/2026

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Key Points

The AutoVDC framework uses vision-language models (VLMs) to automatically detect incorrect annotations in vision datasets, aiming to reduce manual dataset-cleaning effort.
The study evaluates AutoVDC on autonomous-driving object-detection benchmarks (KITTI and nuImages) and creates dataset variants with intentionally injected annotation errors to measure detection performance.
Experiments compare error-detection effectiveness across different VLMs and examine how fine-tuning VLMs affects the cleaning pipeline.
Results indicate strong error detection and improved data-cleaning outcomes, suggesting AutoVDC can raise the reliability and accuracy of large-scale production datasets for autonomous driving.
The work targets the common problem that human labeling is imperfect and often requires multiple costly review iterations to reach usable dataset quality.

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.