Towards Brightness-Robust Unified Anomaly Detection: Training with Multi-Brightness Data

초록

Real-world industrial environments often feature highly variable lighting conditions, causing performance drops in models trained solely under single-illumination data. To address this, we propose a multi-brightness data augmentation approach for One-for-All anomaly detection models, ensuring stable performance across diverse illumination environments. Specifically, we employ Test-Time Adaptation(TTA) from FiCo to generate images with varying brightness, reducing sensitivity to illumination changes. We evaluate performance using AUROC, Pixel-level AUC allowing fine-grained assessment beyond false-positive rates. Experimental results show that multi-brightness-trained models consistently maintain robust performance under novel lighting conditions, alleviating the need for frequent retraining and emphasizing the importance of real-world robustness in anomaly detection. © 2025 IEEE.

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