A PRACTICAL APPROACH TO ELECTRONIC COMPONENT DETECTION ON PCBs USING YOLOv11 DEEP LEARNING MODEL
DOI:
https://doi.org/10.62985/j.huit_ojs.vol26.no2E.415Từ khóa:
YOLOv11, Automated Optical Inspection (AOI), Electronic Component Detection, Printed Circuit Board (PCB)Tóm tắt
In the field of PCB manufacturing, Automated Optical Inspection (AOI) is critical for defect detection. However, traditional methods often fail under varying lighting and complex background conditions. To overcome these issues, this paper presents a deep learning-based approach using the YOLOv11 model for robust electronic component detection. We establish a complete workflow for identifying components such as resistors, capacitors, and ICs, validated through a custom dataset. A key contribution of this work is the comprehensive evaluation of the YOLOv11 architecture specifically optimized for PCB component identification. Validated through a custom dataset, the YOLOv11 model achieved a robust accuracy with an mAP@0.5 of 97.5% on standard hardware. This study demonstrates the feasibility of utilizing YOLOv11 for high-precision Automated Quality Control (QC) in PCB manufacturing. This study demonstrates the feasibility of YOLOv11 in industrial environments and provides a framework for future optimization studies.
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