Detect six types of PCB defects using a trained YOLOv8 model and serve predictions through a Flask-based web interface.

This project implements and compares two YOLOv12 object-detection pipelines for printed-circuit-board (PCB) defect identification. The objective is to detect four major defect types: ...

Abstract: Printed Circuit Boards (PCBs) are the backbone of electronic devices, critical for the functionality and reliability of modern technological applications. Despite advancements in ...

Abstract: The surface defect detection of printed circuit board (PCB) is an important means to ensure the quality of PCB. In this paper, the method of PCB defect detection is deeply studied, and the ...

This new technical paper titled “End-to-end deep learning framework for printed circuit board manufacturing defect classification” is from researchers at École de technologie supérieure (ÉTS) in ...

Board manufacturers are boosting their investment in inspection, test and analytics to meet the increasingly stringent demands for reliability in safety-critical sectors like automotive. This ...

Manufacturers of advanced PCB assemblies know that simultaneously producing cost-competitive products and meeting the quality expectations of customers are vital to their success. Driven by advancing ...