Automated Quality Check of Sensor Data Annotations

2026-03-03 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Intermediate, long

Summary

A new open-source tool has been developed to automate quality checks for multi-sensor data annotations used in training AI algorithms for automated railway vehicles. This tool detects nine common error types in datasets, with five being railway-specific and four being general. Evaluated on the OSDaR23 dataset, the framework achieved 100% precision for six error detection methods and 96-97% precision for the remaining three. The tool significantly reduces the manual workload associated with ensuring high-quality training data, which is critical for the safety-relevant environmental monitoring systems in automated trains, ranging from Grade of Automation (GoA) 2 to GoA 4. The software, named RailLabel-providerkit, is available via pip and integrates with the RailLabel library for accessing the OSDaR23 dataset.

Key takeaway

For AI Scientists developing safety-critical perception systems for automated railway vehicles, integrating the open-source RailLabel-providerkit into your data pipeline is crucial. This tool automates the detection of nine common annotation errors, achieving high precision and significantly reducing manual quality assurance efforts. By adopting this framework, you can accelerate the development and deployment of robust AI models while maintaining the stringent data quality standards required for GoA 2-4 automation levels.

Key insights

Automated quality checks for sensor data annotations significantly reduce manual effort and accelerate AI development for railway safety.

Principles

• Automated error detection improves data quality.
• Domain-specific rules enhance detection accuracy.

Method

The method involves systematically identifying frequent error types, formulating detection rules, implementing them as Python algorithms, and validating precision against manually reviewed errors in a dataset.

In practice

• Utilize RailLabel-providerkit for railway sensor data QA.
• Define strict 3D bounding box size limits.
• Refine annotation requirements based on false positives.

Topics

• Automated Driving
• Sensor Data Annotation
• Data Quality Assurance
• Railway Systems
• Multi-sensor Datasets

Code references

• DSD-DBS/raillabel-providerkit
• DSD-DBS/raillabel
• Vicomtech/weblabel

Best for: AI Scientist, AI Engineer, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.