  Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks
(RNNs), and Generative Adversarial Networks (GANs), our system introduces an
innovative approach to defect detection in manufacturing. This technology
excels in precisely identifying faults by extracting intricate details from
product photographs, utilizing RNNs to detect evolving errors and generating
synthetic defect data to bolster the model's robustness and adaptability across
various defect scenarios. The project leverages a deep learning framework to
automate real-time flaw detection in the manufacturing process. It harnesses
extensive datasets of annotated images to discern complex defect patterns. This
integrated system seamlessly fits into production workflows, thereby boosting
efficiency and elevating product quality. As a result, it reduces waste and
operational costs, ultimately enhancing market competitiveness.
