| _version_ | 1866901733366562816 |
|---|---|
| author | Schmoll, Robert |
| author_facet | Schmoll, Robert |
| contents | <h2>Fluid Flow Dataset</h2> <p> </p> <h2>1. Introduction</h2> <p>A total of three datasets are included here, namely, (1) the synthetic dataset used for model training as mentioned in the paper, (2) the wind tunnel dataset used to validate the segmentation performance of the model, including the corresponding manually labeled labels, and (3) the original wind tunnel experiment dataset.</p> <h2>2. Synthetic differential image dataset for segmentation</h2> <p>To address the lack of semantic segmentation datasets for infrared fluid flow imagery, we created a synthetic dataset from the ScalarFlow dataset, focusing on pixel-level labels suitable for neural network training. Our process, illustrated as shown in the paper, includes generating realistic noise by capturing images under controlled conditions with an Optical Gas Imaging (OGI) camera, followed by image subtraction, normalization, and merging with ScalarFlow data. This method ensures the inclusion of real-world disturbances such as camera jitter effects, enhancing the dataset's robustness and applicability. The dataset, enriched with various data augmentation techniques, comprises over 30,000 images split into training, validation, and testing sets, catering to the rigorous demands of practical applications in fluid dynamics analysis.</p> <h2>3. Wind tunnel dataset</h2> <p>To enable the determination of velocities of fluid flow by using optical flow algorithms, a wind tunnel data set that includes fluid images captured by different cameras was recorded. </p> <p>The fluid flow is created in the wind tunnel and generated by different substances, i.e., dry ice, smoke matches, or paraffin oil. In addition, velocity data collected from the 3D ultrasonic anemometer were used as a reference to evaluate the performance and accuracy of the optical flow algorithms. The fluid flow rate was set at three different velocities in Euclidean space, i.e., 0.7 m/s, 1.4 m/s, and 2.0 m/s.</p> <div> <div>This dataset is captured by using a wind tunnel, the OGI camera FLIR GF320 and a 3D anemometer for obtaining reference flow velocities. Below is the information of the used camera.</div> </div> <h3>FLIR GF320 Camera Info</h3> <table> <tbody> <tr> <td> <div> <div><strong>Parameter</strong></div> </div> </td> <td> <div> <div><strong>Value</strong></div> </div> </td> </tr> <tr> <td> <div> <div>Spectral Range</div> </div> </td> <td> <div> <div>3.2 – 3.4 μm</div> </div> </td> </tr> <tr> <td> <div> <div>Standard Temperature Range</div> </div> </td> <td> <div> <div>–20°C to +350°C</div> </div> </td> </tr> <tr> <td> <div> <div>Accuracy</div> </div> </td> <td> <div> <div> ±1 °C for 0 °C to 100 °C; ±2% > 100 °C</div> </div> </td> </tr> <tr> <td> <div> <div>Lenses</div> </div> </td> <td> <div> <div>24° × 18°</div> </div> </td> </tr> <tr> <td> <div> <div>Resolution</div> </div> </td> <td> <div> <div>320 × 240 Pixel</div> </div> </td> </tr> </tbody> </table> <p> </p> <p>The dataset consists of three parts, i.e., OGI images of fluids/smoke generated from three different substances.</p> <ul> <li>1. Smoke matches dataset<br> <br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>1</td> <td>Smoke matches</td> <td>0.7</td> <td>1</td> <td>143</td> </tr> <tr> <td>2</td> <td>Smoke matches</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>3</td> <td>Smoke matches</td> <td>1.4</td> <td>1</td> <td>145</td> </tr> <tr> <td>4</td> <td>Smoke matches</td> <td>1.4</td> <td>2</td> <td>598</td> </tr> <tr> <td>5</td> <td>Smoke matches</td> <td>2.0</td> <td>1</td> <td>145</td> </tr> <tr> <td>6</td> <td>Smoke matches</td> <td>2.0</td> <td>2</td> <td>596</td> </tr> </tbody> </table> </li> <li>2. Paraffin oil dataset<br><br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>7</td> <td> <div> <div>Paraffin oil</div> </div> </td> <td>0.7</td> <td>1</td> <td>597</td> </tr> <tr> <td>8</td> <td>Paraffin oil</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>9</td> <td>Paraffin oil</td> <td>1.4</td> <td>1</td> <td>597</td> </tr> <tr> <td>10</td> <td>Paraffin oil</td> <td>1.4</td> <td>2</td> <td>597</td> </tr> <tr> <td>11</td> <td>Paraffin oil</td> <td>2.0</td> <td>1</td> <td>597</td> </tr> <tr> <td>12</td> <td>Paraffin oil</td> <td>2.0</td> <td>2</td> <td>597</td> </tr> </tbody> </table> </li> <li>3. Dry ice dataset<br><br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>13</td> <td>Dry ice</td> <td>0.7</td> <td>1</td> <td>598</td> </tr> <tr> <td>14</td> <td>Dry ice</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>15</td> <td>Dry ice</td> <td>1.4</td> <td>1</td> <td>595</td> </tr> <tr> <td>16</td> <td>Dry ice</td> <td>1.4</td> <td>2</td> <td>596</td> </tr> <tr> <td>17</td> <td>Dry ice</td> <td>2.0</td> <td>1</td> <td>185</td> </tr> <tr> <td>18</td> <td>Dry ice</td> <td>2.0</td> <td>2</td> <td>628</td> </tr> </tbody> </table> </li> </ul> <p> </p> <p>We also provide the corresponding 3D anemometer data, which allows the user to convert the pixel displacement from the image to the actual flow rate, as shown in below.</p> <div> <h3>Velocities in m/s and pixel</h3> <table> <tbody> <tr> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Settings of WindChannel</strong></div> </div> </td> <td> <div> <div><strong> Velocity in pixel</strong></div> </div> </td> </tr> <tr> <td> <div> <div>0.7</div> </div> </td> <td>1.88</td> <td>4.57</td> </tr> <tr> <td>1.4</td> <td>2.50</td> <td>9.14</td> </tr> <tr> <td>2.0</td> <td>3.06</td> <td>13.06</td> </tr> </tbody> </table> <p> </p> <h2>4. Wind tunnel segmentation dataset</h2> <p>This part of the dataset is from the dry ice dataset portion of the wind tunnel test dataset described above. And labels are generated by manual labeling for evaluating the performance of the image segmentation model in real-world scenarios, a total of 100 differential images and 100 labels.</p> <p> </p> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_13732831 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Synethic dataset for differential OGI images and wind tunnel dataset Schmoll, Robert <h2>Fluid Flow Dataset</h2> <p> </p> <h2>1. Introduction</h2> <p>A total of three datasets are included here, namely, (1) the synthetic dataset used for model training as mentioned in the paper, (2) the wind tunnel dataset used to validate the segmentation performance of the model, including the corresponding manually labeled labels, and (3) the original wind tunnel experiment dataset.</p> <h2>2. Synthetic differential image dataset for segmentation</h2> <p>To address the lack of semantic segmentation datasets for infrared fluid flow imagery, we created a synthetic dataset from the ScalarFlow dataset, focusing on pixel-level labels suitable for neural network training. Our process, illustrated as shown in the paper, includes generating realistic noise by capturing images under controlled conditions with an Optical Gas Imaging (OGI) camera, followed by image subtraction, normalization, and merging with ScalarFlow data. This method ensures the inclusion of real-world disturbances such as camera jitter effects, enhancing the dataset's robustness and applicability. The dataset, enriched with various data augmentation techniques, comprises over 30,000 images split into training, validation, and testing sets, catering to the rigorous demands of practical applications in fluid dynamics analysis.</p> <h2>3. Wind tunnel dataset</h2> <p>To enable the determination of velocities of fluid flow by using optical flow algorithms, a wind tunnel data set that includes fluid images captured by different cameras was recorded. </p> <p>The fluid flow is created in the wind tunnel and generated by different substances, i.e., dry ice, smoke matches, or paraffin oil. In addition, velocity data collected from the 3D ultrasonic anemometer were used as a reference to evaluate the performance and accuracy of the optical flow algorithms. The fluid flow rate was set at three different velocities in Euclidean space, i.e., 0.7 m/s, 1.4 m/s, and 2.0 m/s.</p> <div> <div>This dataset is captured by using a wind tunnel, the OGI camera FLIR GF320 and a 3D anemometer for obtaining reference flow velocities. Below is the information of the used camera.</div> </div> <h3>FLIR GF320 Camera Info</h3> <table> <tbody> <tr> <td> <div> <div><strong>Parameter</strong></div> </div> </td> <td> <div> <div><strong>Value</strong></div> </div> </td> </tr> <tr> <td> <div> <div>Spectral Range</div> </div> </td> <td> <div> <div>3.2 – 3.4 μm</div> </div> </td> </tr> <tr> <td> <div> <div>Standard Temperature Range</div> </div> </td> <td> <div> <div>–20°C to +350°C</div> </div> </td> </tr> <tr> <td> <div> <div>Accuracy</div> </div> </td> <td> <div> <div> ±1 °C for 0 °C to 100 °C; ±2% > 100 °C</div> </div> </td> </tr> <tr> <td> <div> <div>Lenses</div> </div> </td> <td> <div> <div>24° × 18°</div> </div> </td> </tr> <tr> <td> <div> <div>Resolution</div> </div> </td> <td> <div> <div>320 × 240 Pixel</div> </div> </td> </tr> </tbody> </table> <p> </p> <p>The dataset consists of three parts, i.e., OGI images of fluids/smoke generated from three different substances.</p> <ul> <li>1. Smoke matches dataset<br> <br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>1</td> <td>Smoke matches</td> <td>0.7</td> <td>1</td> <td>143</td> </tr> <tr> <td>2</td> <td>Smoke matches</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>3</td> <td>Smoke matches</td> <td>1.4</td> <td>1</td> <td>145</td> </tr> <tr> <td>4</td> <td>Smoke matches</td> <td>1.4</td> <td>2</td> <td>598</td> </tr> <tr> <td>5</td> <td>Smoke matches</td> <td>2.0</td> <td>1</td> <td>145</td> </tr> <tr> <td>6</td> <td>Smoke matches</td> <td>2.0</td> <td>2</td> <td>596</td> </tr> </tbody> </table> </li> <li>2. Paraffin oil dataset<br><br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>7</td> <td> <div> <div>Paraffin oil</div> </div> </td> <td>0.7</td> <td>1</td> <td>597</td> </tr> <tr> <td>8</td> <td>Paraffin oil</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>9</td> <td>Paraffin oil</td> <td>1.4</td> <td>1</td> <td>597</td> </tr> <tr> <td>10</td> <td>Paraffin oil</td> <td>1.4</td> <td>2</td> <td>597</td> </tr> <tr> <td>11</td> <td>Paraffin oil</td> <td>2.0</td> <td>1</td> <td>597</td> </tr> <tr> <td>12</td> <td>Paraffin oil</td> <td>2.0</td> <td>2</td> <td>597</td> </tr> </tbody> </table> </li> <li>3. Dry ice dataset<br><br> <table> <tbody> <tr> <td> <div> <div><strong>Id</strong></div> </div> </td> <td><strong>Substances</strong></td> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Group</strong></div> </div> </td> <td> <div> <div><strong>Number of frames</strong></div> </div> </td> </tr> <tr> <td>13</td> <td>Dry ice</td> <td>0.7</td> <td>1</td> <td>598</td> </tr> <tr> <td>14</td> <td>Dry ice</td> <td>0.7</td> <td>2</td> <td>597</td> </tr> <tr> <td>15</td> <td>Dry ice</td> <td>1.4</td> <td>1</td> <td>595</td> </tr> <tr> <td>16</td> <td>Dry ice</td> <td>1.4</td> <td>2</td> <td>596</td> </tr> <tr> <td>17</td> <td>Dry ice</td> <td>2.0</td> <td>1</td> <td>185</td> </tr> <tr> <td>18</td> <td>Dry ice</td> <td>2.0</td> <td>2</td> <td>628</td> </tr> </tbody> </table> </li> </ul> <p> </p> <p>We also provide the corresponding 3D anemometer data, which allows the user to convert the pixel displacement from the image to the actual flow rate, as shown in below.</p> <div> <h3>Velocities in m/s and pixel</h3> <table> <tbody> <tr> <td> <div> <div><strong>Velocity in m/s</strong></div> </div> </td> <td> <div> <div><strong>Settings of WindChannel</strong></div> </div> </td> <td> <div> <div><strong> Velocity in pixel</strong></div> </div> </td> </tr> <tr> <td> <div> <div>0.7</div> </div> </td> <td>1.88</td> <td>4.57</td> </tr> <tr> <td>1.4</td> <td>2.50</td> <td>9.14</td> </tr> <tr> <td>2.0</td> <td>3.06</td> <td>13.06</td> </tr> </tbody> </table> <p> </p> <h2>4. Wind tunnel segmentation dataset</h2> <p>This part of the dataset is from the dry ice dataset portion of the wind tunnel test dataset described above. And labels are generated by manual labeling for evaluating the performance of the image segmentation model in real-world scenarios, a total of 100 differential images and 100 labels.</p> <p> </p> </div> |
| title | Synethic dataset for differential OGI images and wind tunnel dataset |
| url | https://doi.org/10.5281/zenodo.13732831 |