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All Journal Jurnal Media Elektro : Journal of Electrical Power, Informatics, Telecommunication, Electronics, Computer and Control System
Bernadectus Yudi Dwiandiyanta
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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PERBAIKAN LOKAL KANDIDAT CACAT TANPA PEMBELAJARAN UNTUK INSPEKSI PCB BERBASIS TEMPLATE DENGAN KLASIFIKASI ROI SELEKTIF Bernadectus Yudi Dwiandiyanta; Silvester Tena
Jurnal Media Elektro Vol 15 No 2 (2026): Oktober 2026
Publisher : Universitas Nusa Cendana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35508/jme.v0i0.30159

Abstract

Printed circuit board (PCB) defect inspection is difficult because many defects are small and visually subtle. This study proposes a template-based inspection method combining image differencing, learning-free local candidate refinement, and selective ROI classification. Initial candidates are obtained from aligned test and template images, then locally refined using grayscale residual information without adding new candidates. Refined ROIs are represented by test, template, and residual patches and classified using MobileNetV3-Small. On DeepPCB, candidate recall at IoU 0.20 increases from 0.3389 to 0.9586 and at IoU 0.30 from 0.1153 to 0.6344. ROI classification achieves 0.9874 accuracy and 0.9855 macro F1. With a rejection threshold of 0.90, coverage remains 0.9762 with macro F1 of 0.9927. The method improves coarse defect localization, although strict bounding-box localization remains limited.
Co-Authors Silvester Tena