In the anti-external force damage inspection of transmission lines, the current lightweight target detection algorithm deployed at the edge has insufficient detection accuracy and slow reasoning speed. To solve the above problems, this study proposes a sparse convolution network (SCN) with global context enhancement for anti-external force damage detection of the power grid, Fast-YOLOv5. Based on the YOLOv5 algorithm, the FasterNet+ network is designed as a new feature extraction network, which can maintain detection accuracy, improve the reasoning speed of the model, and reduce computational complexity. In the bottleneck layer of the algorithm, an ECAFN module with efficient channel attention is designed, which improves the detection effect by adaptively calibrating the feature response in the channel direction, efficiently obtaining the cross-channel interactive information and further reducing the amount of parameters and calculation. The study proposes the detection layer of the sparse convolutional network SCN replacement model with context enhancement to enhance the foreground focus feature and improve the prediction ability of the model by capturing the global context information. The experimental results show that compared with the original model, the accuracy of the improved model is increased by 1.9%, and the detection speed is doubled, reaching 56.2 f/s. The amount of parameters and calculation are reduced by 50% and 53% respectively, which is more in line with the requirements for efficient detection of transmission lines.