Automatic Detection of Discrete Entity Objects in Tunnel Lining Based on Improved Faster R-CNN
As detection accuracy and timeliness of discrete entity objects in tunnel lining are directly related to the oper-ational safety of the tunnel,the automatic image interpretation using image vision technology can greatly improve the effi-ciency and accuracy of detection results.Therefore,by constructing a custom GPR dataset based on the radar image data of discrete entity objects,this paper proposed an improved Faster R-CNN algorithm to automatically detect discrete entity objects in tunnel lining.Firstly,this algorithm improved the feature extraction module of the existing Faster R-CNN net-work,and a new lightweight feature extraction network ResNet_FMBConv was proposed to mine the radar image features in depth.Based on the ResNet_FMBConv network,the existing feature pyramid network(FPN)structure was improved to achieve accurate identification of objects under multiple sizes.Secondly,based on the measured and simulated radar image data,by constructing a custom GPR dataset of discrete entity objects,this paper used the geometric transformation method to enhance the radar image data for the algorithm verification.The results show that the detection accuracy,re-call rate,balanced F1-score and FPS of the improved algorithm are 45.1％,54.0％,49.1％and 21.65 fps respectively when IOU=0.50:0.95.Under the condition that the recall rate remains basically unchanged,the precision rate and balanced F1-score of target detection algorithms such as YOLOv3_spp,SSD,Retinanet and Faster R-CNN are improved by 2％～9％and 1％-6％,respectively.Meanwhile,the experimental results show that the improved feature extraction network ResNet_FMBConv is also superior to the existing target classification networks such as Resnet-50,VGG16,Effi-cientnet_bO and Mobilenetv3.
万方数据
维普
