[Objective] Aiming at the problems of small insulator defect size, susceptibility to complex background interference during detection, and large parameter volume of the baseline model, this paper proposes a lightweight insulator defect detection algorithm based on an improved CPLC-YOLOv8. [Methods] Firstly, the lightweight RepNCSPELAN4-CAA module was designed to replace the C2f module in YOLOv8’s backbone network, reducing parameter quantity while enhancing feature representation capability. Secondly, a small-defect detection layer P2 was added to strengthen the fusion of shallow and deep features, minimizing the loss of small-target information. Subsequently, a lightweight detection head was developed, where 1×1 convolution was employed for channel dimension adjustment and detail-enhanced convolution was utilized to replace conventional 3×3 convolution, achieving parameter sharing and feature enhancement. Finally, the convolutional block attention mechanism was introduced to suppress background interference through dual channel-spatial attention mechanisms, enhancing key feature representation and improving model robustness and detection accuracy. [Results] Experimental results on the custom insulator defect dataset demonstrated that the proposed CPLC-YOLOv8 achieved a mAP@0.5 of 0.928, representing a 2 percentage point improvement over the original YOLOv8. The model parameters were reduced to only 1.72 MB (42.8% reduction compared to YOLOv8), with a compressed model size of 4.12 MB (31.3% compression). Comparative evaluations with classic network models confirmed that CPLC-YOLOv8 exhibited significant advantages in detection accuracy, parameter efficiency, and model compactness, particularly demonstrating superior robustness and generalization capability in small object detection tasks. [Conclusion] The proposed algorithm achieves lightweight model design while maintaining high detection accuracy, making it suitable for deployment on resource-constrained edge devices with promising engineering application prospects. Future work will further explore the integration of multi-scale feature fusion and lightweight techniques to continuously enhance the algorithm’s adaptability and stability in practical power inspection scenarios.