基于深度学习的公路隧道衬砌病害识别方法

任松,朱倩雯,涂歆玥,邓超,王小书

Lining disease identification of highway tunnel based on deep learning

Song REN,Qian-wen ZHU,Xin-yue TU,Chao DENG,Xiao-shu WANG

表 2 不同卷积神经网络的隧道病害检测结果的对比

Tab.2 Comparison of tunnel disease detection results of different convolutional neural networks

模型 病害类型 P R AP mAP t/h V SSD-Mobilenet 裂缝 0.996 0.402 0.411 0.364 $34\dfrac{1}{{6}}$ 2.06 SSD-Mobilenet 渗水 1 0.311 0.316 SSD-Inception V2 裂缝 0.996 0.757 0.769 0.728 $48\dfrac{1}{{3}} $ 3.17 SSD-Inception V2 渗水 0.988 0.673 0.687 R-FCN 裂缝 0.935 0.981 0.983 0.910 $87\dfrac{3}{{4}} $ 10.30 R-FCN 渗水 0.835 0.805 0.833 Faster R-CNN 裂缝 0.796 0.994 0.994 0.890 $84\dfrac{1}{{2}} $ 9.42 Faster R-CNN 渗水 0.886 0.790 0.791