PCB Defect Detection

In today's fast-paced world electronic devices have become an integral part of our daily lives. From smartphones to household appliances, these gadgets rely on Printed Circuit Boards (PCBs) to function effectively. As the demand for more efficient and reliable electronic products continues to rise ensuring the quality of PCBs becomes paramount. However, with the complexity of PCB designs and the increasing volume of manufacturing traditional manual inspection methods fall short in detecting subtle defects leading to potential malfunctions and costly recalls. To address these challenges our project focuses on developing an innovative solution for PCB defect detection using OpenCV and image processing technology. Leveraging the power of computer vision and image processing technology we aim to create an automated system that can accurately identify defects on PCBs enabling manufacturers to ensure the delivery of high-quality products to their customers. The core of our approach involves comparing images of defective PCBs with reference images of non-defective PCBs. By carefully curating a diverse dataset of labelled images consisting of both defective and non-defective PCBs the system will become capable of efficiently discriminating between defective and non-defective PCBs achieving a high level of accuracy and precision. In this synopsis report, we present the methodology and progress of our PCB defect detection project. Additionally, we will discuss the validation and testing procedures to ensure the reliability and effectiveness of our system. Ultimately our project seeks to contribute to the advancement of quality control in the electronics industry. By automating the PCB defect detection process we aim to streamline production reduce costs and enhance product reliability benefiting both manufacturers and end-users. We believe that our innovative approach will pave the way for improved electronic devices that are not only cutting-edge in design but also excel in performance and longevity.