Abstract

This research presents a real-time defect detection system for bottle manufacturing using Machine Learning. The system is designed to detect defects in bottles with high accuracy and efficiency. Leveraging the principles of optics in physics, a specialized method is developed to enhance defect detection, ensuring thorough inspection of bottles for any abnormalities. Implemented on hardware powered by Jetson Nano, the system offers real-time defect detection capabilities, enabling swift identification and rectification of manufacturing flaws. Through the integration of Machine Learning algorithms and optical concepts, this system provides a robust solution for quality control in bottle production processes.

Keywords: Machine Learning, Defect detection, Real-time system, Optical concepts, Jetson Nano.