Recognition Method of Tunnel Lining Defects Based on Deep Learning

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr class="thead"><td class="align_left">Category</td><td class="align_center">Label</td><td class="align_center">Training samples</td><td class="align_center">Test samples</td><td class="align_center">Total</td></tr><tr><td class="thead-hr" colspan="5"><hr/></td></tr><tr><td class="align_left">Not dense</td><td class="align_center">0</td><td class="align_center">470</td><td class="align_center">117</td><td class="align_center">587</td></tr><tr><td class="align_left">Escape</td><td class="align_center">1</td><td class="align_center">480</td><td class="align_center">119</td><td class="align_center">599</td></tr><tr class="table-tr"><td colspan="5"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Classification and size of the data set.</div>

Wireless Communications and Mobile Computing

tab1

Table 1

Table 1: Recognition Method of Tunnel Lining Defects Based on Deep Learning 