Transmission lines are essential infrastructure for power supply in modern society. Bolts, as the main fasteners in transmission lines, are crucial for their proper functioning. This paper proposes an improved YOLOv8-based method for detecting bolt defects in transmission lines. Firstly, a dataset for transmission line bolt defects was constructed, and data augmentation techniques were applied to generate more training samples and increase data diversity. Secondly, due to the high complexity of the background in the data samples, which affects the model's feature representation capability, DynamicConv (Dynamic Convolution Layer) was used to better capture dynamic features in the input data, thereby enhancing the model's feature representation ability. Finally, the ShuffleAttention mechanism was employed, combining channel shuffle and attention mechanism ideas to promote cross-learning between features, improving the model’s representational and generalization capabilities. This mechanism adaptively learns the importance of different features and adjusts feature weights, accordingly, enabling more effective feature fusion and information transfer. Experimental results show that the improved YOLOv8 model performs excellently in detecting bolt defects in transmission lines. Specifically, the improved model achieved a mAP of 86.5% on the self-built dataset, representing a 6.8% increase over the previous version.