Defect Segmentation

Defect segmentation, the task of precisely outlining defective regions in images, is crucial for quality control across diverse industries. Current research emphasizes robust methods for handling unseen defects, often employing deep learning architectures like U-Net, Mask R-CNN, and transformers, with a focus on addressing class imbalance and improving accuracy in challenging scenarios such as nano-scale imaging and complex backgrounds. These advancements are significantly impacting various fields, from semiconductor manufacturing and additive manufacturing to civil infrastructure inspection, enabling automated, high-precision defect detection and ultimately improving product quality and safety.