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The safety of the power system is crucial for the entire energy transmission process. Aiming at the main external force destruction behavior of super large engineering vehicles and fireworks under the transmission line, the single-stage target detection algorithm YOOv5s is improved. First, aiming at the working environment of the transmission line with heavy rain, fog and dust, the restricted contrast adaptive histogram equalization equalization algorithm CLAHE is introduced to defog the image to improve the image contrast; In response to the problem of detecting targets with long distances, a CA attention mechanism was added to the YOLOv5s network to enhance the model's ability to locate targets; Replace the nearest neighbor difference sampling method in the original network with the lightweight universal upsampling operator CARAFE, which better captures feature maps while introducing smaller parameter quantities; Finally, in the feature fusion layer of the network, a GSConv convolution module with channel shuffling idea is used to replace the standard convolution module, reducing the number of model parameters, and then utilizing Slim_ Neck feature fusion structure enhances target attention, achieving the effect of reducing model parameters while improving detection accuracy. The experimental results show that the improved YOLOv5s network improves mAP by 4.4%, reduces parameter count by 3.4%, and reduces weight model memory by 2.7%, proving the effectiveness of the algorithm.