Objective The normal operation of insulators is a crucial part of ensuring the safe and reliable operation of power systems. To promote the automatic detection of insulator defects a lightweight detection model N-YOLOv8 was designed. Methods For network lightweighting a lightweight module called DPHConv was first designed by integrating depthwise separable convolution BN layer Hardswish activation function and residual connections. Second a lightweight module CFI-X was designed by integrating Inception-Bottleneck and C2f. These two modules significantly reduced the number of network parameters at the cost of only a minor loss in detection accuracy. In terms of improving detection accuracy a T-ECA module was designed based on the ECA attention mechanism. Then an attention mechanism PEG was designed by integrating the T-ECA module with a multi-branch parallel structure and a Gather Excite attention module. Furthermore CEIoU _ Loss was designed by combining the ideas of CIoU _ Loss and EIoU _ Loss. Finally Soft-NMS was introduced to replace the NMS in the original network. These improvements effectively boosted the network?? s performance in detecting insulator defects. Results Compared with the YOLOv8n network N-YOLOv8 reduced the number of parameters by 43% and floating-point operations by 37% while achieving a detection accuracy of 91. 7% which was 0. 2% higher than that of the original network. Conclusion N-YOLOv8 delivers high detection performance which can effectively facilitate the deployment of intelligent detection algorithms on UAV platforms and achieve real-time detection of insulator defects.