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Updated: Mar 15, 2026

Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography

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Published on: May 15, 2017

Defect-Intent Ambiguity Addressing for Training-Free Deterministic PCB Defect Localization via Template Selection and

Saiyan Saiyod¹, Woottichai Nonsakhoo¹, Zhengping Li¹

¹Hardware-Human Interface and Communications Laboratory (H2I-Comm Lab), Department of Computer Science, College of Computing, Khon Kaen University, Khon Kaen 40002, Thailand.

Sensors (Basel, Switzerland)

|March 14, 2026

Summary

This study introduces a novel, training-free framework for automated optical inspection (AOI) of printed circuit boards (PCBs). The method efficiently detects defects using reference templates, achieving high precision and recall for reliable pass/fail decisions.

Keywords:

automated optical inspection (AOI)correlation defect detection defect localization difference map image processing printed circuit board (PCB)quantile thresholding structural similarity template selection

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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

Published on: May 28, 2016

Area of Science:

Electrical Engineering
Computer Vision
Manufacturing Automation

Background:

Automated optical inspection (AOI) for printed circuit boards (PCBs) faces challenges with small defects, illumination variations, and misalignment.
Existing methods often require extensive training data and complex models, limiting their adaptability and audibility.

Purpose of the Study:

To present a training-free, reference-based digital image processing framework for AOI of PCBs.
To enable fast, auditable pass/fail decisions for PCB inspection.
To localize small, sparse defects effectively under challenging conditions.

Main Methods:

A reference-based framework comparing query images to defect-free templates without a learning stage.
Coarse-to-fine reference selection using pre-screening and SSIM refinement.
Defect highlighting via a defect-likelihood field fusing SSIM dissimilarity and normalized absolute difference maps.
Connected-component extraction for confidence-ranked bounding box generation.

Main Results:

Achieved high performance metrics: Precision = 0.9663, Recall = 0.9987, F1 = 0.9822 at the best-F1 operating point.
Demonstrated average precision of 0.984 under a box-matching protocol.
Reported Precision-Recall and FROC curves for threshold selection.

Conclusions:

The proposed training-free framework offers a robust and efficient solution for PCB AOI.
The method's interpretability and audibility support reliable defect detection and decision-making.
This approach addresses key challenges in localizing defects under varying conditions.