Pavement Crack Detection Method Based on Deep Learning Models

<table class="table-group" id="tab1"><tr><td><table class="table"><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr class="thead"><td class="align_left">Symbol</td><td class="align_center">Description</td></tr><tr><td class="thead-hr" colspan="2"><hr/></td></tr><tr><td class="align_left">TP</td><td class="align_center">TP refers to the number of cracks that are correctly classified as cracks</td></tr><tr><td class="align_left">TN</td><td class="align_center">TN refers to the number of backgrounds that are correctly classified as backgrounds</td></tr><tr><td class="align_left">FP</td><td class="align_center">FP refers to the number of backgrounds that are incorrectly identified as backgrounds</td></tr><tr><td class="align_left">FN</td><td class="align_center">FN refers to the number of cracks that are incorrectly identified as the background</td></tr><tr class="table-tr"><td colspan="2"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Basic concept in the evaluation indicators.</div>

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Table 1

Table 1: Pavement Crack Detection Method Based on Deep Learning Models 