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Simulation of a Scaled Assembly Process with Collaboration of a Robotic Arm and Monitoring through a Vision System for Quality Control

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Few-Shot Adaptation of Foundation Vision Models for PCB Defect Inspection.

Sang-Jeong Lee¹

¹Multimodal AX Business Team, LG CNS Co., Ltd., Seoul 07795, Republic of Korea.

Journal of Imaging

|November 26, 2025

Summary

This summary is machine-generated.

Visual Prompt Tuning (VPT) significantly improves Automated Optical Inspection (AOI) for Printed Circuit Boards (PCBs) by achieving high accuracy and reliability with minimal data. This parameter-efficient fine-tuning strategy offers a scalable solution for defect detection in manufacturing.

Keywords:

Low-Rank Adaptation (LoRA)Visual Prompt Tuning (VPT)automated optical inspection (AOI)few-shot learning parameter-efficient tuning (PEFT)printed circuit board (PCB)

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Area of Science:

Computer Vision
Machine Learning
Manufacturing Technology

Background:

Automated Optical Inspection (AOI) for Printed Circuit Boards (PCBs) faces challenges due to limited labeled data and domain shifts.
These limitations impede the development of accurate deep learning models for manufacturing defect detection.

Purpose of the Study:

To benchmark Parameter-Efficient Fine-Tuning (PEFT) strategies for PCB defect classification.
To evaluate the performance and reliability of Linear Probe, Low-Rank Adaptation (LoRA), and Visual Prompt Tuning (VPT) on foundation vision models.

Main Methods:

Systematic benchmarking of three PEFT strategies (Linear Probe, LoRA, VPT) applied to CLIP-ViT-B/16 and DINOv2-S/14 models.
Evaluation on six-class PCB defect classification tasks under few-shot (k=5, 10, 20) and full-data regimes.
Analysis of model performance, reliability, and parameter efficiency.

Main Results:

Visual Prompt Tuning (VPT) achieved 0.99 ± 0.01 accuracy and 0.998 ± 0.001 macro-AUPRC, reducing classification error by ~65% compared to Linear and LoRA.
VPT tuned fewer than 1.5% of backbone parameters, demonstrating high efficiency.
VPT showed superior adaptation to rare defect types (Spur, Spurious Copper) and maintained high performance on common defects (Short, Pinhole).

Conclusions:

Prompt-based adaptation (VPT) offers a favorable trade-off between accuracy, efficiency, and reliability for PCB defect inspection.
VPT is a scalable strategy for factory-level AOI, enabling rapid deployment of robust defect detection models with scarce labeled data.