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