# Drone vs. Bird Detection: Deep Learning Algorithms and Results from a Grand Challenge
## 5 EagleDrone Team
### Experimental Evaluation  
An initial training step was done on a GPU in Google Colab. Due to file constraints on Google Colab, only 3000 images were chosen for the first iteration of the training, 150 images were chosen for the validation set. Training was done for 30 epochs and the validation loss was monitored. The checkpoints where the validation loss decreased was saved and it ensured that the saved checkpoint was not overfitting. The training loss decreased as expected and validation score was close to the training score and the nature of the curve indicated that overfitting had not started till the last few epochs.  
After the first round of training, it was seen that in images like Figure 13, where the background does not contain the sky, the drone was not detected. Figure 14 contains blue and white background and the model can easily detect the drone. This is due to the fact that in the training images, there were mostly objects with sky as the background so the model had considered that fact to classify the object. Also, it classified birds as drones. This might be because training images with birds were very low and contained drones that looked like birds. Thus the model could not understand the fact that those were a separate class.  
Figure 12: Detection of Multiple Objects.  
Figure 13: Example of drone detection issue.  
To address this problem, the loss on all the images were calculated. Images with objects being unclassified will have a higher loss and this would have given an idea of which images contain the most unclassified objects. 3000 of such images were selected using a linear sampling probability. Thus the one having the higher error had the greater chance of selection. It was retrained using the same method as before.  
This variation of hard modelling removed the previous problem but it unclassified other things as seen in Figure 15[46]. Measuring Catastrophic Forgetting in Neural Networks might be a reason for why this was happening. When the model received the new images, it fine tuned the weights to predict the objects in the new set of images. But as a result of the fine tuning, the model did not store the information that was present before fine tuning.  
YOLOv5 is the best one stage object detector that is available at the moment. The higher models could not be fit into the Colab, so YOLOv5s which is the smallest of the models in their model base was tried. Figure 16 shows that YOLOv5 did not give excellent results and as it was unable to perform, further investigation on one-stage object detectors was not done.  
ESRGAN is one of the most successful super-resolution technique available. In this model, a Generative Adversarial Network is used. The generator is a model that generates  
Figure 16: Left image is the ground truth. In the right image, YOLOv5 is not able to detect.  
Figure 14: Drone detection is feasible in white and blue background.  
Figure 15: Model classifying random objects.  
a super-resolution image from a low-resolution image. The discriminator tries to predict if the image generated is a super-resolution image or a high-resolution image. The generator tries to fool the discriminator while the discriminator helps the generator to improve by letting it know where changes need to be done. The authors chose 80 \(\times\) 40 resolution images and downsized them to 20 \(\times\) 10 images and tried to bring them back to 80 \(\times\) 40. The results were not promising, as it can be observed in Figure 17, although the approach performed better than normal bi-linear interpolation. In the original paper, the image sizes were 128 \(\times\) 128 and they were extended to 512 \(\times\) 512. In this case, the low-resolution image size are about one-eighth of the original size. The 20 \(\times\) 10 image quality was already degraded, hence applying this technique was not beneficial.