RAD: A Comprehensive Dataset for Benchmarking the Robustness of Image Anomaly Detection

arXiv:2406.07176v1 Announce Type: new
Abstract: Robustness against noisy imaging is crucial for practical image anomaly detection systems. This study introduces a Robust Anomaly Detection (RAD) dataset with free views, uneven illuminations, and blurry collections to systematically evaluate the robustness of current anomaly detection methods. Specifically, RAD aims to identify foreign objects on working platforms as anomalies. The collection process incorporates various sources of imaging noise, such as viewpoint changes, uneven illuminations, and blurry collections, to replicate real-world inspection scenarios. Subsequently, …

abstract anomaly anomaly detection arxiv benchmarking cs.cv current dataset detection detection methods free image imaging practical robust robustness study systems type