In response to the problem of poor detection accuracy in current transmission line inspections, a transmission line inspection algorithm based on YOLOv8 (GCAF-YOLOv8) is proposed. Firstly, designed a global channel spatial attention module GCSA to enhance the expressive power of input feature maps. This module combines channel attention, channel shuffling, and spatial attention mechanisms to capture global dependencies in feature maps. Secondly, introduce the StarBlocks structure from StarNet and fuse it with the existing C2f modules in Neck and Backbone to construct a new C2f_Star module, which improves the accuracy of object detection and reduces the overall parameter count of the model. Then, it is proposed to replace the traditional convolution in the baseline model with the ADown convolution module to improve the detection accuracy of subtle features. Finally, combining Focal Loss with the original CIoU in YOLOv8, a Focal CIoU Loss function is designed to solve the problem of class imbalance and improve the accuracy of detecting box position prediction. The experimental results show that the proposed GCAF-YOLOv8 model has improved detection accuracy P by 3.3% and average accuracy detection mean mAP by 3% compared to the original model. It can effectively detect various defects in power components and foreign objects on transmission lines.