CNN Based Image Classification of Malicious UAVs

CNN Based Image Classification of Malicious UAVs

Blog Article

Unmanned Aerial Vehicles (UAVs) or drones have found a wide range of useful applications in society over the past few years, but there has also been a growth in the use of UAVs for malicious purposes.One way to manage this issue is to allow reporting of malicious UAVs (e.g., through a smartphone application) with the report including a photo of the UAV.It would be useful to able to automatically identify the type of UAV within the image in terms of the manufacturer and specific product identification using a trained image classification model.

In this paper, we discuss the collection of images for three popular UAVs at different elevations and different Windows - Guides distances from the observer, and using different camera zoom levels.We then train 4 image classification models based upon Convolutional Neural Networks (CNNs) using this UAV image dataset Travel Cups and the concept of transfer learning from the well-known ImageNet database.The trained models can classify the type of UAV contained in unseen test images with up to approximately 81% accuracy (for the Resnet-18 model), even though 2 of the UAVs represented in the UAV image dataset are visually similar, and the fact that the UAV image dataset contains images of UAVs that are a significant distance from the observer.This provides a motivation to expand the study in the future to include more UAV types and other usage scenarios (e.g.

, UAVs carrying loads).