Application Research of a Deep Learning Model Integrating CycleGAN and YOLO in PCB Infrared Defect Detection

TL;DR

This paper proposes a novel data augmentation framework combining CycleGAN and YOLOv8 to improve PCB infrared defect detection under limited IR data conditions, achieving near-supervised performance.

Contribution

It introduces a cross-modal unpaired image translation method to generate high-fidelity pseudo-IR images, enhancing defect detection accuracy with limited real IR samples.

Findings

Augmented model outperforms limited-data models significantly.

Pseudo-IR synthesis approaches supervised-level detection performance.

Effective in low-data industrial inspection scenarios.

Abstract

This paper addresses the critical bottleneck of infrared (IR) data scarcity in Printed Circuit Board (PCB) defect detection by proposing a cross-modal data augmentation framework integrating CycleGAN and YOLOv8. Unlike conventional methods relying on paired supervision, we leverage CycleGAN to perform unpaired image-to-image translation, mapping abundant visible-light PCB images into the infrared domain. This generative process synthesizes high-fidelity pseudo-IR samples that preserve the structural semantics of defects while accurately simulating thermal distribution patterns. Subsequently, we construct a heterogeneous training strategy that fuses generated pseudo-IR data with limited real IR samples to train a lightweight YOLOv8 detector. Experimental results demonstrate that this method effectively enhances feature learning under low-data conditions. The augmented detector significantly outperforms models trained on limited real data alone and approaches the performance benchmarks of fully supervised training, proving the efficacy of pseudo-IR synthesis as a robust augmentation strategy for industrial inspection.