A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation

<table class="table-group" id="tab6"><tr><td><table class="table"><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr class="thead"><td class="align_left">Number</td><td class="align_center">Methods</td><td class="align_center">mAP</td></tr><tr><td class="thead-hr" colspan="3"><hr/></td></tr><tr><td class="align_left">A</td><td class="align_center">CSPDarkNet53 + SPP + PANet</td><td class="align_center">75.4</td></tr><tr><td class="align_left">B</td><td class="align_center">A + attention block</td><td class="align_center">77.91 (↑2.51)</td></tr><tr><td class="align_left">C</td><td class="align_center">A + FDS block</td><td class="align_center">76.45 (↑1.05)</td></tr><tr><td class="align_left">D</td><td class="align_center">Our method (B + C)</td><td class="align_center">81.09 (↑5.69)</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Ablation experiments on the NRSD-MN dataset.</div>

Computational Intelligence and Neuroscience

tab6

Table 6

Table 6: A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation 