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

<table class="table-group" id="tab3"><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">79.27</td></tr><tr><td class="align_left">B</td><td class="align_center">A + attention block</td><td class="align_center">80.6 (↑1.33)</td></tr><tr><td class="align_left">C</td><td class="align_center">A + FDS block</td><td class="align_center">81.38 (↑2.11)</td></tr><tr><td class="align_left">D</td><td class="align_center">Our method (B + C)</td><td class="align_center">86.74 (↑7.47)</td></tr><tr class="table-tr"><td colspan="3"><hr class="tbody-hr"/></td></tr></table></td></tr></table>

<div>Ablation studies on the RSDDs dataset.</div>

Computational Intelligence and Neuroscience

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Table 3

Table 3: A Model for Surface Defect Detection of Industrial Products Based on Attention Augmentation 