Research Article
Deeper and Mixed Supervision for Salient Object Detection in Automated Surface Inspection
Table 5
SDMS network’s performance on RSDDs, road cracks, and NEU datasets. We used the same model and hyperparameter settings in the training.
| Dataset | RSDDs | Road cracks | NEU | Settings |
| Model | | | | | | | The same as the SDMS-D, which is shown in Table 3 | SDMS | Output0 | 0.01132 | 0.5526 | 0.03044 | 0.6973 | 0.00891 | 0.7927 | Output1 | 0.00549 | 0.7842 | 0.01955 | 0.8169 | 0.00616 | 0.8229 | Output2 | 0.00527 | 0.7835 | 0.01934 | 0.8148 | 0.00593 | 0.8249 | Output0+ | 0.00906 | 0.6884 | 0.02728 | 0.6549 | 0.00735 | 0.7913 | Output1+ | 0.00602 | 0.7888 | 0.01494 | 0.7805 | 0.00611 | 0.8258 | Output2+ | 0.00575 | 0.7882 | 0.01479 | 0.7786 | 0.00590 | 0.8306 |
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