知识嵌入与超网络引导的少样本变电站电表缺陷图像生成方法
Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method
AI总结：
针对变电站电表缺陷图像标注样本稀缺问题，提出将知识嵌入、超网络引导条件控制集成到稳定扩散管道的方法，能逼真可控合成缺陷图像，优于现有基线，提升下游检测器mAP等，为工业检测提供数据合成方案。
摘要：
变电站电表对监测和确保电网稳定运行至关重要，但裂纹等物理缺陷检测常因标注样本严重稀缺而受阻。为应对少样本生成挑战，提出新框架，将知识嵌入和超网络引导的条件控制集成到稳定扩散管道中，从有限数据实现逼真且可控的缺陷图像合成。首先用DreamBooth风格知识嵌入微调稳定扩散主干，缩小自然图像预训练模型与工业设备的领域差距。其次引入几何裂纹建模模块参数化缺陷属性以生成空间约束控制图。还设计轻量级超网络根据控制图和高级缺陷描述符动态调制扩散模型去噪过程。实验表明该方法显著优于现有增强和生成基线，降低Frechet Inception Distance(FID)32.7%，增加多样性指标，训练下游缺陷检测器时将平均精度均值(mAP)提高15.3%，为缺陷样本稀缺的工业检测系统提供实用高质量数据合成解决方案。
英文摘要：
Substation meters play a critical role in monitoring and ensuring the stable operation of power grids, yet their detection of cracks and other physical defects is often hampered by a severe scarcity of annotated samples. To address this few-shot generation challenge, we propose a novel framework that integrates Knowledge Embedding and Hypernetwork-Guided Conditional Control into a Stable Diffusion pipeline, enabling realistic and controllable synthesis of defect images from limited data. First, we bridge the substantial domain gap between natural-image pre-trained models and industrial equipment by fine-tuning a Stable Diffusion backbone using DreamBooth-style knowledge embedding. This process encodes the unique structural and textural priors of substation meters, ensuring generated images retain authentic meter characteristics. Second, we introduce a geometric crack modeling module that parameterizes defect attributes--such as location, length, curvature, and branching pattern--to produce spatially constrained control maps. These maps provide precise, pixel-level guidance during generation. Third, we design a lightweight hypernetwork that dynamically modulates the denoising process of the diffusion model in response to the control maps and high-level defect descriptors, achieving a flexible balance between generation fidelity and controllability. Extensive experiments on a real-world substation meter dataset demonstrate that our method substantially outperforms existing augmentation and generation baselines. It reduces Frechet Inception Distance (FID) by 32.7%, increases diversity metrics, and--most importantly--boosts the mAP of a downstream defect detector by 15.3% when trained on augmented data. The framework offers a practical, high-quality data synthesis solution for industrial inspection systems where defect samples are rare.
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