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

2026-01-01

arXiv.org

AI Summary

This work addresses the challenge of limited real infrared data for training deep learning models in PCB defect detection by proposing a cross-modal data augmentation framework that integrates CycleGAN with YOLOv8. The method introduces unsupervised image-to-image translation to synthesize high-fidelity pseudo-infrared images from abundant unpaired visible-light images, preserving both defect structures and thermal distribution characteristics. These generated images are then combined with scarce real infrared samples to train a lightweight detector. Experimental results demonstrate that the proposed approach significantly outperforms baselines trained solely on real infrared data and achieves performance close to fully supervised settings, thereby validating the effectiveness and practicality of cross-modal synthesis for industrial infrared defect inspection.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• infrared defect detection
• data scarcity
• PCB inspection
• cross-modal augmentation

Innovation

• CycleGAN
• YOLOv8
• infrared defect detection
• cross-modal data augmentation
• pseudo-IR synthesis