改进RCF算法的电缆绝缘层边缘检测

目前电缆绝缘层厚度检测算法主要采用图像处理技术提取出绝缘层的边缘轮廓,此类算法存在绝缘层边缘过宽和边缘不连续等问题,影响了后续的检测精度.为提高绝缘层测量精度,新算法基于RCF算法进行改进,在模型的4、5阶段采用空洞卷积,增大模型的感受野;并在侧输出网络加入尺度增强模块(SEM模块)和由浅到深的级联网络,增加侧输出图像...

Edge detection of cable insulation based on improved RCF algorithm

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 4 th and 5 th 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 withthe 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.