Defect Detection System

Defect detection systems aim to automate the identification of flaws in manufactured products or infrastructure using image analysis, improving efficiency and quality control. Current research emphasizes the use of deep learning models, such as convolutional neural networks (CNNs) and variations incorporating attention mechanisms, often combined with traditional image processing techniques or ensemble methods to enhance robustness and accuracy, even with limited labeled data. These advancements are impacting various industries, from manufacturing (tires, composite materials, 3D printed objects) to infrastructure inspection (railways, concrete), enabling more efficient quality assurance and predictive maintenance.