DATABASE-DRIVEN DYNAMIC FORMS MANAGEMENT SYSTEM

The Product Defect Detection System in the manufacturing industry, ensuring product quality is critical.Traditional methods of defect detection often involve manual inspection, which is time-consuming, costly, and prone to human error.The product defect detection system that leverages deep learning and mobileNetV2 Architecture techniques to enhance accuracy and efficiency in identifying defective products.This project aims to develop an product defect detection system using deep learning techniques integrated into a web application built with Flask.The model is trained on a diverse dataset of images representing both defective and nondefective products, enabling it to learn subtle patterns and anomalies that indicate defects.We explore various architectures and training strategies, comparing their performance in terms of detection accuracy, processing speed, and robustness to variations in product appearance and lighting conditions.The system leverages convolutional neural networks (CNNs) to accurately identify defects in product images, offering a scalable solution that can be deployed in various industrial environments.The proposed deep learning-based system demonstrates significant improvements over traditional defect detection methods, achieving higher accuracy and reducing false positive and negative rates.Additionally, the system's ability to continuously learn and adapt to new types of defects makes it a versatile tool for dynamic manufacturing environments.This research highlights the potential of deep learning to revolutionize quality control processes, offering a scalable and reliable solution for defect detection in various industries.The proposed system significantly reduces inspection time, improves accuracy, and can be easily integrated into existing production lines.