AutoVDC: Automated Vision Data Cleaning Using Vision-Language Models
This addresses the laborious and expensive manual data cleaning process for autonomous driving systems, though it is incremental as it applies existing VLMs to a specific domain.
The paper tackles the problem of imperfect annotations in autonomous driving vision datasets by introducing AutoVDC, a framework that uses Vision-Language Models to automatically detect errors, achieving high performance in error detection on KITTI and nuImages datasets.
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.