This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrate...This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrates a telecentric camera with a Cartesian scanning platform to capture distortion-free,high-resolution PCB images,which are stitched into a single orthographic composite.A YOLO-based detection model,trained on a dataset of 270 PCB images across 23 component classes with data augmentation,identifies and localizes electronic components with a mean average precision of 0.932.Detected components are automatically matched to corresponding 3D CAD models from a part library and assembled within a Fusion 360 environment,producing a 3D digital replica.Experimental results show a similarity score of 0.894 and dimensional deviations below 2%,outperforming both SensoPart image measurement and manual vernier methods.The proposed approach bridges optical metrology and CAD automation,providing a scalable solution for AI-assisted reverse engineering,digital archiving,and intelligent manufacturing.展开更多
基金funded by the Ratchadaphiseksomphot Endowment Fund,Chulalongkorn University grant number:RSF-AnH-69-06-21-01.
文摘This paper presents an automated imaging-to-CAD reconstruction system that combines telecentric vision and deep learning for high-accuracy digital reconstruction of printed circuit boards(PCBs).The framework integrates a telecentric camera with a Cartesian scanning platform to capture distortion-free,high-resolution PCB images,which are stitched into a single orthographic composite.A YOLO-based detection model,trained on a dataset of 270 PCB images across 23 component classes with data augmentation,identifies and localizes electronic components with a mean average precision of 0.932.Detected components are automatically matched to corresponding 3D CAD models from a part library and assembled within a Fusion 360 environment,producing a 3D digital replica.Experimental results show a similarity score of 0.894 and dimensional deviations below 2%,outperforming both SensoPart image measurement and manual vernier methods.The proposed approach bridges optical metrology and CAD automation,providing a scalable solution for AI-assisted reverse engineering,digital archiving,and intelligent manufacturing.
