缺陷绝缘子检测是智能电网运行维护中的关键任务之一。针对航拍绝缘子图像中存在的多目标、多尺度检测难题，提出了一种基于改进CenterNet架构的缺陷绝缘子检测方法。该方法以无锚点检测器为基础框架，创新性地集成了三项关键技术：首先，设计了扩展特征增强模块，通过引入扩展卷积有效扩展特征感受野，显著提升了模型对多尺度目标特征的捕捉能力；其次，在网络中嵌入卷积块注意力机制，动态优化特征通道权重分布，既提高了检测精度又优化了计算效率；最后，采用多尺度特征金字塔结构，实现了多层次特征的融合与互补。实验验证显示，该方法在复杂场景下的缺陷绝缘子检测中表现卓越，平均精度达到95.17 %，各项指标均显著优于现有主流算法，充分证明了其在电力巡检实际应用中的优势。

Defective insulator detection is one of the critical tasks in the operation and maintenance of smart grids. To address the challenges of multi-target and multi-scale detection in aerial insulator images, a defective insulator detection method is proposed based on an improved CenterNet architecture. The method adopts an anchor-free detector as the foundational framework and innovatively integrates three key technologies. Firstly, an expanded feature enhancement module is designed, which effectively enlarges the feature receptive field through dilated convolutions, significantly improving the model's ability to capture multi-scale target features. Secondly, a convolutional block attention mechanism is embedded into the network to dynamically optimize the weight distribution of feature channels, enhancing both detection accuracy and computational efficiency. Finally, a multi-scale feature pyramid structure is employed to achieve the fusion and complementarity of multi-level features. Experimental validation demonstrates that this method excels in defective insulator detection under complex scenarios, achieving an average precision of 95.17 %, with all metrics significantly outperforming existing mainstream algorithms, fully proving its advantages in practical applications for power line inspection.