A Deep Learning- and AI-Enhanced Telecentric Vision Framework for Automated Imaging-to-CAD Reconstruction

Toa Saito¹, Kantawatchr Chaiprabha², Kosuke Takano¹, Gridsada Phanomchoeng², Ratchatin Chancharoen^2,*
1 Department of Information and Computer Sciences, Faculty of Information Technology, Kanagawa Institute of Technology, Kanagawa, Tokyo, Japan
2 Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, Thailand
* Corresponding Author: Ratchatin Chancharoen. Email: email

Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.077356

Received 08 December 2025; Accepted 06 February 2026; Published online 02 March 2026

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Abstract

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.