Report 7 Brandon Silva

Updates Training on half of the dataset compared to the full dataset produced similar results Binary labels seem to help the network learn faster, but evens out to same fscores as the gaussian labels, given other parameters are the same. Best run: 640x640 input dimensions, 1 * 10-5 learning rate Fscore is calculated with predicated pixels counting as a true positive if within 5 pixels of the ground truth center

Best (Binary):

Binary:

Gaussian:

Predicted: Ground Truth:

Predicted: Ground Truth:

Predicted: Ground Truth:

Predicted: Ground Truth:

Conclusions Low F-Score threshold values provide the best results, as the network is learning to detect UAVs, but predictions are low values (0.1-0.2) A threshold of 0.05 obtained an F-Score of 0.6915 The best score with threshold of 0.3 was 0.6790 Background values are very close to zero, so lower threshold still has better scores With a bit more fine tuning to the hyperparameters, larger resolution size, and the model, I should be able to get similar results to original paper 0.77 F-Score for target detections only

Next Use dense optical flow frames as input to network, to help reduce noise Tracking: known methods and what novel solution I can create