The current cable insulation layer thickness detection algorithm mainly uses image processing technology to extract the edge contour of the insulation layer. Such algorithms have problems such as excessively wide insulation layer edges and discontinuous edges, which affect the subsequent detection accuracy. In order to improve the measurement accuracy of the insulation layer, new algorithm is based on the RCF (Richer Convolutional Features) algorithm to improve, in the 4th and 5th stages of the model, the cavity convolution is used to increase the receptive field of the model; and the scale enhancement module (SEM module) is added to the side output network. And the cascade network from shallow to deep to increase the detailed information of the side output image. The model was trained through the self-made cable insulation data set. The results show that the improved model has 0.821 and 0.842 in the optimal scale of the data set (ODS) and the optimal scale of a single picture (OIS), respectively, and the average accuracy is 0.799. Compared with the RCF model ODS and OIS, the algorithm is improved by 0.008 and 0.01 respectively, and the detection accuracy is improved by 0.021. The performance of the model is further verified on the Berkeley University Data Set (BSD500) data set, where ODS and OIS are 0.810 and 0.825, respectively. Compared with the RCF model, the ODS and OIS of this algorithm are improved by 0.009 and 0.006, respectively.