Fast-DG2GAN: A Computationally Efficient DG2GAN Variant for Industrial Injection Molding Splay Defect Generation

Synthetic data generation is a potential solution for addressing limited data in manufacturing defect detection. In injection molding of automotive tubes, surface defects such as splay present as white or silver streaks in the tube’s texture, that are difficult to capture in sufficient quantity for training robust object detection models. This study proposes Fast-DG2GAN: a DG2GAN based, computationally optimized, generative style defect generator for manufacturing defect images. Fast-DG2GAN achieves a 56% reduction in training time relative to the original DG2GAN, completing training in 334.9 min compared to 768.0 min, while maintaining comparable image quality metrics with a best FID score of 132.83 and IS 1.45 ± 0.08. Key contributions to this DG2GAN variant include depth-wise separable convolutions, reduced residual blocks, and automatic mixed precision (AMP) training, to improve training time. Training stabilization techniques include perceptual loss, feature matching, and exponential moving average of weights (EMA). Legacy Generative Adversarial Network (GAN) architectures are benchmarked for feasibility and include WGAN, DCGAN, and FastGAN, for fine-grained defect image generation essential to downstream object detection. Metrics, such as Inception Score (IS) and Fréchet Inception Distance (FID) are used for quantitative performance evaluation. This study highlights the potential of GAN-generated datasets to augment real-world training for defect detection models.