PCB-Fire: Automated Classification and Fault Detection in PCB

TL;DR

This paper introduces an automated method for detecting missing components and classifying faults in printed circuit boards using pixel theory and object detection techniques to improve manufacturing quality.

Contribution

The paper presents a novel algorithm combining pixel theory and object detection for efficient PCB fault detection and classification.

Findings

Effective detection of missing PCB components

Improved classification accuracy of faults

Reduced time and effort in PCB quality assurance

Abstract

Printed Circuit Boards are the foundation for the functioning of any electronic device, and therefore are an essential component for various industries such as automobile, communication, computation, etc. However, one of the challenges faced by the PCB manufacturers in the process of manufacturing of the PCBs is the faulty placement of its components including missing components. In the present scenario the infrastructure required to ensure adequate quality of the PCB requires a lot of time and effort. The authors present a novel solution for detecting missing components and classifying them in a resourceful manner. The presented algorithm focuses on pixel theory and object detection, which has been used in combination to optimize the results from the given dataset.