Advancing PCB Quality Control : Harnessing YOLOv8 Deep Learning for Real-Time Fault Detection
Printed Circuit Boards (PCBs) are materials used to connect components to one another to form a working circuit. PCBs play a crucial role in modern electronics by connecting various components. The trend of integrating more components onto PCBs is becoming increasingly common, which presents signi...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tech Science Press
2024
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/46043/3/TSP_CMC_54439%20%281%29.pdf http://ir.unimas.my/id/eprint/46043/ https://www.techscience.com/cmc/v81n1/58313 https://doi.org/10.32604/cmc.2024.054439 |
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| Institution: | Universiti Malaysia Sarawak |
| Language: | English |
| Summary: | Printed Circuit Boards (PCBs) are materials used to connect components to one another to form a working circuit.
PCBs play a crucial role in modern electronics by connecting various components. The trend of integrating more
components onto PCBs is becoming increasingly common, which presents significant challenges for quality control
processes. Given the potential impact that even minute defects can have on signal traces, the surface inspection of
PCB remains pivotal in ensuring the overall system integrity. To address the limitations associated with manual
inspection, this research endeavors to automate the inspection process using the YOLOv8 deep learning algorithm
for real-time fault detection in PCBs. Specifically, we explore the effectiveness of two variants of the YOLOv8
architecture: YOLOv8 Small and YOLOv8 Nano. Through rigorous experimentation and evaluation of our dataset
which was acquired from Peking University’s Human-Robot Interaction Lab, we aim to assess the suitability of
these models for improving fault detection accuracy within the PCB manufacturing process. Our results reveal
the remarkable capabilities of YOLOv8 Small models in accurately identifying and classifying PCB faults. The
model achieved a precision of 98.7%, a recall of 99%, an accuracy of 98.6%, and an F1 score of 0.98. These findings
highlight the potential of the YOLOv8 Small model to significantly improve the quality control processes in PCB
manufacturing by providing a reliable and efficient solution for fault detection. |
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