TY  - EJOU
AU  - Saito, Toa 
AU  - Chaiprabha, Kantawatchr 
AU  - Takano, Kosuke 
AU  - Phanomchoeng, Gridsada 
AU  - Chancharoen, Ratchatin 

TI  - A Deep Learning- and AI-Enhanced Telecentric Vision Framework for Automated Imaging-to-CAD Reconstruction
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2026
VL  - 146
IS  - 3
SN  - 1526-1506

AB  - 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.
KW  - Metrology; telecentric vision; YOLO; imaging-to-CAD reconstruction

DO  - 10.32604/cmes.2026.077356