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

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. Used for paper: Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection https://github.com/stevefoy/Turfgrass-Divot-Object-Detection @inproceedings{IMVIP2024, author = {Stephen Foy and Simon McLoughlin}, title = {Advancing Turfgrass Maintenance with Synthetic Data for Divot Detection}, booktitle = {Irish Machine Vision and Image Processing Conference (IMVIP)}, year = {2024} } Contents of the Zip File: synthDivot_416x416 Folder: Train and validation subfolders 1200 RGB PNG images Corresponding masks for each image Bounding box data in YOLO .txt format synthDivot_608x608 Folder: Train and validation subfolders 1200 RGB PNG images Bounding box data in YOLO .txt format下述数据集系使用Blender合成制作。研究团队已采用YOLO V3目标检测技术对该数据集开展基础分析，以识别草皮凹坑（divot）或损伤区域。 本数据集用于以下研究论文： 该论文为2024年爱尔兰机器视觉与图像处理会议（IMVIP）的收录论文，作者为斯蒂芬·福伊（Stephen Foy）与西蒙·麦克劳克林（Simon McLoughlin），论文标题为《借助合成数据实现草皮凹坑检测以推进草坪养护》。 项目开源仓库地址：https://github.com/stevefoy/Turfgrass-Divot-Object-Detection 该数据集压缩包的内容如下： synthDivot_416x416 文件夹： 包含训练与验证子文件夹，共计1200张RGB格式PNG图像，配套每张图像对应的掩码文件，以及YOLO格式的.txt边界框标注数据。 synthDivot_608x608 文件夹： 包含训练与验证子文件夹，共计1200张RGB格式PNG图像，以及YOLO格式的.txt边界框标注数据。