Insulators are an essential part of overhead transmission lines in distribution networks. Accurate identification of insulator images by drone aerial photography is an important prerequisite for defect detection and fault diagnosis. Aiming at the problem of small insulator targets and complex backgrounds in images, an algorithm for insulators identification on overhead transmission lines in distribution networks based on multi-scale dense networks is proposed in this paper. First, use the K-means algorithm to analyze the target frame of the dataset to obtain a suitable anchor frame. Second, replace the residual module in the basic network with a dense connection module to enhance the multiplexing and fusion of network feature information. At the same time, add a spatial pyramid pooling module and optimize multi-scale feature fusion to predict insulators. Finally, replace the original loss function with a loss function that combines the cross-entropy function and the Focal loss function to construct an aerial inspection image data set and perform experiments. The experimental results showed that the algorithm accuracy is improved by about 12 percentage points and has a stronger robustness than the original algorithm, which meets the requirements of the grid inspection for insulator identification.