Defect Inspection

Defect inspection, crucial for quality control in manufacturing and other industries, aims to automatically identify flaws in materials and products using advanced imaging and analysis techniques. Current research emphasizes the development and refinement of deep learning models, particularly convolutional neural networks (CNNs), often incorporating attention mechanisms and feature fusion strategies to improve accuracy and speed, especially for low-contrast or variable-sized defects. These advancements, along with the creation of larger, more semantically rich datasets and exploration of alternative methods like deflectometry, are driving improvements in automated inspection systems, leading to increased efficiency and reduced costs across various sectors.