![]() Update the package index: sudo apt-get update.You'd want to train the model with larger dataset/fine tine hyper-parameter, etc.You'd want to use pre-trained model to detect drones in a given image.Prediction: Specific instructions to simply use pre-trained model right off the bat and go with the workflow.Training: Well documented instructions from scratch to getting the model trained.The installation instructions are separated into two categories depending on your use-case:.The entire source code is well documented and uses type hinting for more stability.Simple Intuitive API is provided to help in prediction task with full control over tolerance of detecting drones.Dataset used to train the model with clear instructions are provided in the case you'd want to train over a larger dataset.Multiple drones can be detected from an image.A pre-trained model is included in the repository ready to be used out of the box for drone detections. ![]() ![]() ![]() I took the challenge by researching online of different techniques of detecting objects from a given picture, and with a prior of experience of using Fast R-CNN architecture in my workplace, I just went with it to see how it fares against drone detection. One of the key challenges of the problem statement was to detect any UAV or Drone from a given image. Me and my partner Nilesh participated in a Hackathon called, MoveHack which had one of the problem statement of Drone and UAV traffic management. A deep learning neural net model to detect drone/drones from a given picture using Using Fast R-CNN architecture via Keras-Retinanet Implementation. ![]()
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