Objective: To accurately detect the obstacles in the field so that our robot can avoid these obstacles.
Dataset: 53 images of obstacles in the field
Use AlexeyAB darknet implementation of Yolov4, which has support for Windows.
- Set up GPU and darknet
- Set up virtual environment
- Install CUDA and cuDNN
- Set up vcpkg library manager
- Set up darknet on Windows
- Generate augmented images
- 4 augmentations per given image were generated
- Augment bounding box in the image augmentation process
- Train and test model
- Follow darknet tutorial to train on custom dataset
- Visualize predictions
The detailed steps can be found in the Jupyter Notebooks.