UAV Payload Detection Using Deep Learning and Data Augmentation

In recent years, the technology behind Unmanned Aerial Vehicles (UAVs) has continually advanced. However, with these developments, malicious activities employing UAVs have also been on the rise. Within this study, Deep Learning (DL) algorithms are utilized to detect and classify UAVs transporting payload based on the sound they release. In order to exercise DL algorithms on a set of data, a sufficient amount of audio data is necessary to obtain a more reliable result. So UAV sound recordings have been collected alongside the use of data augmentation to secure a satisfactory sample size for testing purposes. Afterward, a feature-based classification was applied to the groups of audio identifying each UAV’s payload (or lack thereof). Lastly, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Convolutional Recurrent Neural Network(CRNN) are utilized in analyzing the final data-set. They are evaluated for their abilities to correctly categorize the unloaded, one payload, and two payload of UAV classes and noise class solely through audio. As a result, MFCC showed the best performance in CNN, RNN, and CRNN, which are 0.9493, 0.8133, and 0.9174 accuracies. Our contribution to this study is that a cost-efficient data collection method was applied by utilizing laptop microphones. Moreover, DL technology was used in UAV payload detection, whereas neural network was used in prior study. Also, the best feature for UAV payload detection with the three DL technologies was found. The limitation of the paper is that only two UAV models and one kind of payload were used to collect data. Diverse UAVs and payload are expected to be used to collect data in future works.