A YOLO-based Real-time Packaging Defect Detection System

Abstract

Managing the quality of products is one of the primary concerns in manufacturing production to obtain better operational efficiency in factories. In recent years, there have been numerous different approaches for improving product quality management in manufacturing. Each method has certain advantages and limitations, and the common goal is to bring the best efficiency in managing product quality before delivering them to consumers. In this paper, we introduce an approach to creating a real-time packaging defect detection system based on deep learning techniques intending to automatically detect defective packaged products in industrial quality control of packages. To be more precise, we present a real-time defect detection system to help classify product quality automatically based on the YOLO (You only look once) algorithm. The system can be integrated into factories and production lines, helping to optimize efficiency and save operating costs.