<![CDATA[Insulators are critical components in power transmission and distribution systems, where any defects can lead to severe operational failures and power outages. To enhance inspection efficiency, unmanned aerial vehicles (UAVs) are increasingly used for aerial monitoring. However, the quality of images captured by drones is often compromised due to hardware limitations, motion blur, and complex environmental backgrounds, which significantly reduces the performance of deep learning-based defect detection methods. This study proposes an improved insulator defect detection model based on the YOLOv8n architecture, optimized for accuracy and efficiency in low-quality image scenarios and suitable for deployment in resource-constrained environments. The model introduces two major modifications. First, a Slim-Neck module employing Ghost-Shuffle Convolution (GSConv) replaces standard convolutions to substantially reduce computational cost while preserving rich feature representations. Second, an Efficient Multi-Scale Attention (EMA) module is integrated into the neck to enhance multi-scale feature fusion by maintaining per-channel information without dimensionality reduction, improving the model’s ability to extract discriminative features. Experimental results demonstrate that the proposed model achieves a precision of 92.0%, recall of 88.6%, mAP@0.5 of 92.1%, and an inference speed of 161.29 FPS. Furthermore, it reduces parameter count by 10.8% and computational load by 8.6% compared to the baseline, validating its suitability for real-time UAV-based inspections. The model also outperforms existing methods in detecting insulator defects, particularly in challenging conditions involving blur and complex backgrounds.]]>
