Turfgrass Divot Dataset (Synthetic ) for divot detection object detection system

<p>The dataset provided below has been synthetically created using Blender. A fundamental analysis on this data was conducted utilizing the YOLO V3 object detection technique to identify divots or areas of damage.</p> <p>Used for paper:&nbsp;</p> <p>Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection</p> <p>https://github.com/stevefoy/Turfgrass-Divot-Object-Detection</p> <pre><span>@inproceedings</span>{<span>IMVIP2024</span>, <span>author</span> = <span><span>{</span>Stephen Foy and Simon McLoughlin<span>}</span></span>, <span>title</span> = <span><span>{</span>Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection<span>}</span></span>, <span>booktitle</span> = <span><span>{</span>Irish Machine Vision and Image Processing Conference (IMVIP)<span>}</span></span>, <span>year</span> = <span><span>{</span>2024<span>}</span></span> }</pre> <p><strong>Contents of the Zip File</strong>:</p> <ul> <li> <p><strong>synthDivot_416x416 Folder</strong>:</p> <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Corresponding masks for each image</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> <li> <p><strong>synthDivot_608x608 Folder</strong>:</p> <ul> <li>Train and validation subfolders</li> <li>1200 RGB PNG images</li> <li>Bounding box data in YOLO <code>.txt</code> format</li> </ul> </li> </ul> <p>&nbsp;</p> <p>&nbsp;</p>