Research Article
Multilayer Feature Extraction of AGCN on Surface Defect Detection of Steel Plates
Table 2
Performance comparison (mIoU).
| Model | White iron scale | Roll printing | Scratch | Scarring | Embroidery skin | Average |
| Faster R-CNN [8] | 0.7962 | 0.7204 | 0.8386 | 0.7602 | 0.8982 | 0.8027 | SegNet [39] | 0.8250 | 0.6835 | 0.8532 | 0.8622 | 0.8824 | 0.8213 | PSPNet [40] | 0.8032 | 0.7282 | 0.8419 | 0.8358 | 0.9047 | 0.8228 | YOLOv4 [14] | 0.8068 | 0.7025 | 0.8793 | 0.8524 | 0.8856 | 0.8253 | DeepLab+ [23] | 0.8271 | 0.7139 | 0.8748 | 0.8437 | 0.8754 | 0.8270 | RefineNet [41] | 0.8265 | 0.7280 | 0.8786 | 0.8613 | 0.8410 | 0.8271 | AGCN (ours) | 0.8485 | 0.7188 | 0.9098 | 0.8762 | 0.9367 | 0.8580 |
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