A Deep Generative Approach for Neural Augmentation of Heat Sink Surface Defects

The need to obtain a diverse set of high-fidelity defect cases has recently gained attention for enhancing defect detection performance. However, collecting actual defect samples remains challenging due to the high cost along with considerable data imbalance across different defect scenarios. To address these issues, this study proposed a deep learning-based approach for generating synthetic defect cases based on specified defect information. More specifically, a conditional generative adversarial network (cGAN) was developed to generate high-fidelity synthetic images of heat sink surface defects conditioned on user-defined defect types, locations, and sizes. The generative performance of the proposed model was quantitatively evaluated. The proposed model achieved a Learned Perceptual Image Patch Similarity (LPIPS) of 0.1738 and a Fréchet Inception Distance (FID) of 23.5749. Qualitative results validated that the proposed model successfully generated a variety of synthetic defect cases in accordance with the provided defect specifications. These findings demonstrate the potential of the proposed method as a neural augmentation strategy to enhance defect detection performance across various industrial applications.