Anomaly Segmentation Metrics for anomalib

This project proposes novel evaluation metrics for anomaly segmentation in computer vision, taking into account pixel-level and spatial information. The aim is to provide a more comprehensive evaluation of anomaly segmentation algorithms, aiding researchers and practitioners in selecting and fine-tuning models. The first proposed metric is the False Positive Blob Relative Volume (FP-BRV), which accounts for the visual nuisance of false positive pixels, complementing the Per-Region Overlap (PRO). The proposed metric will be evaluated on popular anomaly segmentation public datasets and visually validated. Milestones: prototype implementation, testing and validation on public datasets, production implementation, optimization/unit testing/documentation, and research paper writing. See "Section 5 Detailed project proposal" in the PDF for details and a timeline.