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

<table class="table-group" id="tab4"><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">81.38</td></tr><tr><td class="align_left">B</td><td class="align_center">A + attention block</td><td class="align_center">81.59 (↑0.21)</td></tr><tr><td class="align_left">C</td><td class="align_center">A + FDS block</td><td class="align_center">81.79 (↑0.41)</td></tr><tr><td class="align_left">D</td><td class="align_center">Our method (B + C)</td><td class="align_center">82.00 (↑0.62)</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Ablation experiments on the BSData dataset.</div>

Computational Intelligence and Neuroscience

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Table 4

Table 4: A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation 