In order to increase inspection efficiency of high-voltage transmission lines， an insulator detection algorithm based on improved Faster Region-based Convolutional Neural Network （Faster-RCNN） was proposed. Firstly， the Selective Kernel Neural Network （SKNet） with attention mechanism was added to feature extraction network to make the network focus on learning the insulator features related channels. Secondly， the Filter Response Normalization （FRN） layer was used to replace the original Batch Normalization （BN） layer to avoid the model falling into the gradient saturation region. Finally， the Distance Intersection Over Union （DIoU） was used to replace the original Intersection Over Union （IoU） to accurately express the positions of the feature candidate region boxs. The open source aerial insulator dataset was enhanced by the operations such as translation， rotation， Cutout and CutMix. The dataset was expanded to 3 000 images， and 2 500 images of them were randomly selected as the training set， and the remaining 500 images were selected as the test set. Compared with the original Faster-RCNN algorithm， the average accuracy of the proposed algorithm is improved by 3.46 percentage points， and the average recall is improved by 2.76 percentage points. Experimental results show that the proposed algorithm has high detection accuracy and stability， and can meet the requirements of the application scenarios of power line insulator detection.