Visual Quality Inspection

Visual quality inspection uses computer vision and machine learning to automate the detection of defects in manufactured products and infrastructure, aiming for increased efficiency, accuracy, and safety compared to manual methods. Current research emphasizes the development of efficient deep learning models, such as optimized convolutional neural networks (CNNs) and generative adversarial networks (GANs), often incorporating explainable AI (XAI) techniques to enhance user trust and facilitate model improvement. This field is crucial for various industries, improving product quality, reducing costs, and enabling safer inspection procedures in challenging environments, from manufacturing lines to large-scale infrastructure assessments.