To address the issues of high computational complexity and poor real-time performance in current insulator defect detection algorithms, this paper proposes a lightweight insulator detection algorithm based on an improved YOLOv8n. Firstly, the C2f module is improved using a Universal Inverted Bottleneck (UIB) module, which reduces the computational load while maintaining high feature extraction capability by leveraging the concept of depthwise separable convolutions and a flexible module architecture. Secondly, the YOLOv8 backbone and neck networks are optimized using the ADown structure to further compress the model's size. Lastly, a new Focaler-CIoU loss function is employed to enhance the model's detection accuracy. Experimental results show that the improved model achieves an mAP@0.5 of 98.0%, with a 41% reduction in parameters, a 35% decrease in computational load, and a 38% reduction in model size compared to the original YOLOv8n model, enabling accurate insulator defect detection while maintaining a lightweight design.