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

• URL: http://arxiv.org/abs/2601.00237v1
• Date: Thu, 01 Jan 2026 07:01:47 GMT
• Title: Application Research of a Deep Learning Model Integrating CycleGAN and YOLO in PCB Infrared Defect Detection
• Authors: Chao Yang, Haoyuan Zheng, Yue Ma,
• Abstract summary: This paper proposes a cross-modal data augmentation framework integrating CycleGAN and YOLOv8.<n>We leverage CycleGAN to perform unpaired image-to-image translation, mapping abundant visible-light PCB images into the infrared domain.<n>We construct a heterogeneous training strategy that fuses generated pseudo-IR data with limited real IR samples to train a lightweight YOLOv8 detector.
• Score: 7.407155043542133
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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