Aiming at the current problems of cumbersome operation, low efficiency and large variation in repeated measurements of highvoltage cable insulation layer quality inspection, a new type of cable insulation layer inspection device is designed, and a high-voltage cable insulation layer image segmentation method based on improved U-Net is proposed. Firstly, the backbone feature extraction network is replaced with the VGG16 network, the weights trained by VGG16 in the Pascal VOC2012 dataset are used as the pre-training weights in combination with the transfer learning, the adaptive feature weighting mechanism is incorporated in the jump connections by using the channel attention module, as well as the grouped convolution is added in the up-sampling process, which improves the semantic segmentation accuracy. Next, the insulating layer image segmentation is performed using the trained optimal weights, the contour region features are extracted and binarised, and the contour region is filled using the connected region algorithm. Finally, the complete insulation layer segmentation image is generated by fusing the original image and the segmented region. The experimental results show that the mean intersection-over-union and mean pixel accuracy reach 99. 56% and 99. 81%, which is a significant improvement over the original network effect, and verifies the effectiveness of the method on the segmentation of the insulation layer of high-voltage cables.