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

<table class="table-group" id="tab5"><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">70.35</td></tr><tr><td class="align_left">B</td><td class="align_center">A + attention block</td><td class="align_center">72.08 (↑1.73)</td></tr><tr><td class="align_left">C</td><td class="align_center">A + FDS block</td><td class="align_center">71.22 (↑0.87)</td></tr><tr><td class="align_left">D</td><td class="align_center">Our method (B + C)</td><td class="align_center">74.67 (↑4.32)</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Ablation experiments on the NEU-DET dataset.</div>

Computational Intelligence and Neuroscience

tab5

Table 5

Table 5: A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation 