Scene-Domain-Adaptive Sample Expansion for Few-Shot Insulator Defect Detection

Insulator types and materials vary substantially across power system inspection scenarios, while damage defects occur infrequently; consequently, defect images that match a target insulator type and operating environment are often difficult to obtain. Existing open-source insulator image datasets provide limited coverage of equipment types, scene backgrounds, and defect morphologies. Their direct use for detector training may therefore cause domain mismatch and poor generalization. To address these limitations, this study proposes a scene-domain-adaptive sample expansion method for few-shot damaged-insulator detection. The method adapts a general-purpose pretrained diffusion model to the insulator inspection domain and incorporates three-dimensional (3D) structural constraints to generate targeted samples of damaged insulators. First, low-rank adaptation (LoRA) is used for scene-domain adaptation, enabling the generation model to learn the characteristic geometry, appearance, and material properties of insulators. Second, a 3D model of the target insulator is constructed, and physical damage simulation and edge extraction are applied to obtain geometric guidance maps containing shed boundaries and fracture contours. These maps constrain the locations and shapes of the generated defects. Finally, the geometric guidance is injected into the diffusion process to synthesize damaged-insulator images, which are combined with limited real samples to train detectors for damaged-insulator instances, which were evaluated exclusively on real validation images. Experimental results show that adding a moderate number of generated samples enriches the scarce defect features in the real dataset and improves detector performance. In the mixture-ratio experiment using YOLOv8, [email protected] increased by 8.2 percentage points. Additional experiments with multiple detectors yielded performance gains of varying magnitudes, demonstrating that the generated samples provide an effective supplement to limited, real-world data. The proposed method alleviates the scarcity of insulator defect samples and offers a practical data-augmentation strategy for intelligent inspection of power equipment.