The project endeavours to build a landmark detection framework to detect course markers enabling an autonomous vehicle find an optimal trajectory to complete a circuit. This work will aid in the controller that will be used to navigate a closed course in minimal time. We use the EfficientDet family of object detectors for detection of the landmarks. The model will be deployed on a Raspberry Pi and accelerated using a Coral USB accelarator after conversion to the tflite format.
Why we chose the EfficientDet family of models and which compound scaling coefficient you should choose
"Data! Data! Data! I can't make bricks without clay" - Sir Arthur Conan Doyle. What he and I both need and can't get enough of
Setting up your environment and installing tensorflow a.k.a the need for military precision in resolving dependency conflicts
Just press play. A jupyter notebook run in Google Colab is pure joy :) How a Jupyter Notebook will make it easier for you to test and tune your model quickly
How to determine which EfficientDet model is best suited to run with limited computational resources and specified accuracy constraints
Staying on track - Mapping landmarks of known geometry using detections from a monocular camera and visualizing them in the BEV frame