Unmanned aerial vehicle intelligent inspection is a relatively advanced technology in road disease detection. A road crack detection method based on a dual pyramid network is proposed to meet the needs of both accuracy and real-time detection of road cracks under complex backgrounds and noise interference in drone aerial images. Using improved MobileNetv3_small encoding features to lightweight the network. A stepped feature guidance method is designed for skip connections, combined with a mixed domain attention mechanism to form three different scale feature pyramid fusion networks, efficiently transmitting multi-scale contextual features. Finally, a parallel scale aware pyramid fusion module is designed deep in the network to transmit more detailed encoding features. In addition, the joint constraints of focal loss and dice loss are optimized through weight correction, improving the network's ability to handle imbalanced class data during training. The experimental results on a self-made dataset show that the F1 score and average intersection to union ratio of this double pyramid network reach 87.51% and 79.84%, respectively, which are 2.39 percentage points and 3.47 percentage points higher than CPFNet. The model parameter count is significantly reduced, and the performance and generalization of this method are verified on the CFD public dataset.