Research on Pavement Crack Segmentation Method Based on Res2Unet-CBAM Network
Crack pavement damage is a common problem in urban roads,the efficient acquisition of pavement crack information in numerous pavement information images is a key point of current research in the field.In order to achieve higher accuracy in image recognition analysis,this paper combined the field of deep learning and carried out the pavement crack image segmentation work based on the Res2Unet-CBAM network model,analyzed the experimental results of the public dataset and compared various semantic segmentation models such as U-Net,Res2Net,and so on.Based on this,a Res2-Unet multi-scale pavement crack segmentation network model was designed.A new Res2Unet-CBAM net-work model was constructed for the segmentation task of pavement crack images by analyzing the com-mon channel attention module and introducing the CBAM channel attention module into the Res2-Unet segmentation model.Comparing the experimental results of Res2Unet-CBAM model with those of other deep learning models,the results show that this model has better image segmentation performance.
deep learningpavement detectionattention mechanismcrack semantic segmentation
万方数据
维普
