A lightweight benchmark workflow for evaluating object detection robustness under real-world image corruptions.
The project focuses on measuring how detection models degrade under realistic visual conditions such as:
- motion blur
- low-light noise
- compression artifacts
- occlusion
- sensor degradation
Most object detection benchmarks evaluate clean datasets only.
However, real-world deployment environments contain significant image corruption and instability.
This project aims to provide a reproducible evaluation workflow for robustness analysis.
- corruption pipeline
- robustness scoring
- degradation reports
- YOLO integration
- visualization utilities
- benchmark comparisons
Early prototype / research workflow.# validron-robustness-benchmark Robustness evaluation workflow for object detection models under real-world image corruptions.