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Benyun authored Sep 10, 2023
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Expand Up @@ -51,158 +51,20 @@ Learning-based visual inspection, integrated with unmanned robotic system, offer
<img src="./frame.png">
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<table border="1" style="border-collapse: collapse; width: 100%;">
<thead>
<tr>
<th>Dataset</th>
<th>Num. of Images</th>
<th>Resolution</th>
<th>Data Collection Platform</th>
<th>Category</th>
<th>Scenario</th>
<th>Material</th>
<th>Experiments</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="2">RDD-2018</td>
<td rowspan="2">9053</td>
<td rowspan="2">600x600</td>
<td rowspan="2">Smartphones</td>
<td>Crack</td>
<td rowspan="2">Pavement</td>
<td rowspan="2">Asphalt</td>
<td rowspan="2">- SSD</td>
</tr>
<tr>
<td>Corrosion</td>
</tr>
<tr>
<td rowspan="2">RDD-2019</td>
<td rowspan="2">13135</td>
<td rowspan="2">600x600</td>
<td rowspan="2">Smartphones</td>
<td>Crack</td>
<td rowspan="2">Pavement</td>
<td rowspan="2">Asphalt</td>
<td rowspan="2">- SSD</td>
</tr>
<tr>
<td>Corrosion</td>
</tr>
<tr>
<td rowspan="2">RDD-2020</td>
<td rowspan="2">26336</td>
<td>600x600</td>
<td rowspan="2">Smartphones</td>
<td>Crack</td>
<td rowspan="2">Pavement</td>
<td rowspan="2">Asphalt</td>
<td rowspan="2">- SSD</td>
</tr>
<tr>
<td>720x720</td>
<td>Pothole</td>
</tr>
<tr>
<td rowspan="4">RDD-2022</td>
<td rowspan="4">47420</td>
<td>512x512</td>
<td>Smartphones</td>
<td rowspan="4">-</td>
<td rowspan="4">Pavement</td>
<td rowspan="4">Asphalt</td>
<td rowspan="4">-</td>
</tr>
<tr>
<td>600x600</td>
<td>Hand-held cameras</td>
<td>Crack</td>
</tr>
<tr>
<td>720x720</td>
<td>UAV cameras</td>
<td>Pothole</td>
</tr>
<tr>
<td>3650x2044</td>
<td>Google street view</td>
</tr>
<tr>
<td rowspan="2">PID</td>
<td rowspan="2">7237</td>
<td rowspan="2">640x640</td>
<td rowspan="2">Crawled from Internet</td>
<td rowspan="2">Crack</td>
<td rowspan="2">Pavement</td>
<td rowspan="2">Asphalt</td>
<td>a. YOLOv2</td>
</tr>
<tr>
<td>b. Fast R-CNN</td>
</tr>
<tr>
<td>Murad</td>
<td>2620</td>
<td>up to 838x809</td>
<td>Smartphones</td>
<td>Crack</td>
<td>Pavement</td>
<td>Asphalt</td>
<td>- Faster R-CNN</td>
</tr>
<tr>
<td rowspan="2">CODEBRIM</td>
<td rowspan="2">1590</td>
<td rowspan="2">up to 6000x4000</td>
<td>Hand-held cameras</td>
<td>Crack</td>
<td rowspan="2">Bridge</td>
<td rowspan="2">Concrete</td>
<td>a. MetaQNN</td>
</tr>
<tr>
<td>UAV Cameras</td>
<td>Corrosion</td>
<td>b. ENAS</td>
</tr>
<tr>
<td rowspan="9"><strong>CUBIT</strong></td>
<td rowspan="9"><strong>5527</strong></td>
<td rowspan="9">-</td>
<td rowspan="9"><strong>Cameras in Unmanned Systems</strong></td>
<td rowspan="9">-</td>
<td rowspan="9">-</td>
<td rowspan="9">-</td>
<td><strong>a. Faster R-CNN</strong></td>
</tr>
<tr>
<td><strong>b. PP-YOLO</strong></td>
</tr>
<tr>
<td><strong>c. PP-YOLOv2</strong></td>
</tr>
<tr>
<td><strong>d. YOLOX</strong></td>
</tr>
<tr>
<td><strong>e. YOLOv5</strong></td>
</tr>
<tr>
<td><strong>f. YOLOv7</strong></td>
</tr>
<tr>
<td><strong>g. YOLOv6</strong></td>
</tr>
<tr>
<td><strong><em>h. YOLOv6+GIPFPP(ours)</em></strong></td>
</tr>
<tr>
<td><strong><em>i. Real-site experiment</em></strong></td>
</tr>
</tbody>
</table>
## The Comparison between Existing Bounding-box-level Defect Dataset with CUBIT

| Dataset | Num. of Images | Resolution | Data Collection Platform | Category | Scenario | Material | Experiments |
|---------------|----------------|----------------------|------------------------------------|------------------------|--------------------------|----------------------|-----------------------------------------------|
| RDD-2018 | 9053 | 600x600 | Smartphones | Crack, Corrosion | Pavement | Asphalt | SSD |
| RDD-2019 | 13135 | 600x600 | Smartphones | Crack, Corrosion | Pavement | Asphalt | SSD |
| RDD-2020 | 26336 | 600x600, 720x720 | Smartphones | Crack, Pothole | Pavement | Asphalt | SSD |
| RDD-2022 | 47420 | 512x512, 600x600, 720x720, 3650x2044 | Smartphones, Hand-held cameras, UAV cameras, Google street view | Crack, Pothole | Pavement | Asphalt | - |
| PID | 7237 | 640x640 | Crawled from Internet | Crack | Pavement | Asphalt | YOLOv2, Fast R-CNN |
| Murad | 2620 | up to 838x809 | Smartphones | Crack | Pavement | Asphalt | Faster R-CNN |
| CODEBRIM | 1590 | up to 6000x4000 | Hand-held cameras, UAV Cameras | Crack, Corrosion | Bridge | Concrete | MetaQNN, ENAS |
| **CUBIT** | **5527** | **4624x3472 and 8000x6000** | **Cameras in Unmanned Systems** | **Crack, Spallinig, Moisture** | **Building (65%), Pavement (29%), Bridge (6%)** | **Concrete, Asphalt, Stone** | **Faster R-CNN, PP-YOLO, PP-YOLOv2, YOLOX, YOLOv5, YOLOv7, YOLOv6, YOLOv6+GIPFPP(ours), Real-site experiment** |




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