Regular inspection of insulators of transmission lines is an indispensable task， while traditional manual inspections have problems such as low efficiency and high work intensity. Therefore， this paper designs an improved U-Net model to realize the segmentation of insulators， and uses an improved YOLOv5 to realize the positioning of blasting insulators in complex backgrounds. Based on the U-Net image semantic segmentation model， this paper proposes an improved network structure SERes-Unet. The model introduces residual structure to reduce the influence of gradient disappearance and structural information loss in the convolution process， and introduces an attention mechanism to correct feature weights， thereby improving network performance. In order to realize the detection of blasting insulators on high-resolution images， it is proposed to cut the pictures and then detect them， and then filter through Non-Maximum suppression（NMS） to obtain the positions of all blasting insulators in the image. The article designs multiple sets of experimental controls to verify the effectiveness and efficiency of the model. In the end， the method achieves an insulator segmentation accuracy of 0.96， a blasting insulator detection accuracy of 0.97， and a recall rate of 0.99.