改进的HRNet应用于路面裂缝分割与检测

Improved HRNet applied to segmentation and detection of pavement cracks

针对利用传统卷积神经网络进行路面裂缝分割时存在准确率低、信息丢失及边缘模糊的问题,本文提出了基于改进HRNet模型的路面裂缝分割算法。模型在原始HRNet的基础上进行改进,主干网络部分采用DUC模块代替双线性插值上采样;下采样改为passthrough layer代替原始卷积;在模型解码部分,进行逐级上采样的同时引入SE-Block,对不同特征层的融合重新标定权重。通过与原始HRNet及传统卷积神经网络U-Net对比可知,本文算法在公共数据与自制数据集上的分割精度表现优秀,F1分值分别达到了91.31%和78.69%,可以很好地满足实际工程的需求。

Aiming at the problems of low accuracy,loss of information and blurred edges in the traditional convolutional neural network for pavement crack segmentation,a pavement crack segmentation algorithm based on the improved HRNet model is proposed.The model is improved on the basis of the original HRNet,the backbone network part uses DUC module instead of bilinear interpolation;downsampling is changed to passthrough layer to replace the original convolution,SE-Block is introduced while performing step-by-step upsampling to re-calibrate the fusion of different feature layers.Comparing with the original HRNet and the other traditional convolutional neural networks U-Net,it can be concluded that the segmentation accuracy of this algorithm is the best on public data and self-made data sets,with F1 score reaching 91.31%and 78.69%respectively,proving that the algorithm can be very good to meet the needs of actual engineering.