Data Augmentation Using HDA-SinGAN Under Limited CT Scan Samples for Internal Battery Defect Detection

Accurate detection of internal defects in battery CT images is essential for ensuring the safety and reliability of energy storage systems. However, the limited number and imbalance of defect samples—especially rare cases such as electrode cracks and metallic inclusions—greatly affect the performance of deep learning models. To address this challenge, we propose HDA-SinGAN, a lightweight image generation framework capable of producing realistic and diverse defect samples from a single image while simultaneously reducing the parameters by 19.6%. HDA-SinGAN integrates High-Frequency Loss, a Dynamic Channel Scaling (DCS) mechanism, and an Adaptive Feature Aggregation (AFA) module, which together preserve fine details and enhance spatial feature extraction. Experimental results on industrial battery CT datasets show that HDA-SinGAN outperforms SinGAN in both image quality and efficiency. For cracks, it achieves improvements of 2.1% in SIFID, 2.5% in SSIM, and 27.5% in PSNR; for metallic inclusions, the gains are 1.8%, 1.9%, and 19.3%, respectively. Incorporating the generated samples into YOLOv8-n training further increases the mean Average Precision (mAP) by 10.6% for cracks and 9.4% for inclusions. These results indicate that HDA-SinGAN effectively balances training datasets and enhances the detection of rare battery defects, providing a lightweight and efficient solution for industrial applications.