YOLO V7: Advancing Printed Circuit Board Defect Detection and the Quality Assurance

The PCB serves as a critical component in the project’s electronic system, to achieve specific functionality. This paper introduces the YOLO v7 deep learning method for defect identification and quality inspection of circuit boards. This process is essential for identifying and rectifying defects that could compromise the functionality and reliability of electronic devices. Traditional methods fall short due to subjectivity and time constraints. YOLO v7’s real-time object detection prowess is harnessed to automate and enhance the process. A collection of circuit board datasets is annotated and preprocessed. YOLO v7 is fine-tuned on this data. Designing and implementing a computerized system that is capable of classification and identifying flaws in circuit boards is the primary objective of this study. This requires creating a powerful deep-learning model to analyze circuit board defect images accurately, localizing defects down to the pixel level, and bridging the gap between traditional inspection methods and modern demands.