instance segmentation of defects

**Instance Segmentation of Defects** is the **detection and pixel-level delineation of each individual defect instance** — combining object detection (where is each defect) with semantic segmentation (what shape is it), distinguishing separate defects even when they overlap or touch. **Key Architectures** - **Mask R-CNN**: Extends Faster R-CNN with a mask prediction branch for each detected instance. - **YOLACT**: Real-time instance segmentation combining detection and prototype masks. - **SOLOv2**: Directly segments instances without explicit detection, using dynamic convolutions. - **Cascade Mask R-CNN**: Multi-stage refinement for higher-quality masks. **Why It Matters** - **Individual Counting**: Counts separate defects even when they touch or are closely spaced. - **Per-Defect Metrics**: Computes area, shape, orientation for each individual defect independently. - **Kill Probability**: Per-instance analysis enables individual kill probability estimation for each defect. **Instance Segmentation** is **giving each defect its own identity** — separately outlining and classifying every individual defect for precise per-defect analysis.