Deep Learning-Based Acoustic Recognition of UAVs in Complex Environments

In recent years, UAV technology has developed rapidly and has been widely applied across various fields. However, as the adoption of civilian UAVs continues to grow, there has been a corresponding rise in the number of black flights by UAVs, which may cause criminal activities and privacy and security issues, so it has become necessary to recognize UAVs in the airspace in order to deal with potential threats. This study recognizes UAVs based on the acoustic signals of UAV flights. Since there are various acoustic interferences in the real environment, more efficient acoustic recognition techniques are needed to meet the recognition needs in complex environments. Aiming at the recognition difficulties caused by the overlap of UAV sound and the background noise spectrum in low signal-to-noise ratio environments, this study proposes an improved lightweight ResNet10_CBAM deep learning model. The optimal performance of MFCC in low SNR environments is verified by comparing three feature extraction methods, Spectrogram, Fbank, and MFCC. The enhanced ResNet10_CBAM model, with fewer layers and integrated channel and spatial attention mechanisms, significantly improved feature extraction in low SNR conditions while reducing model parameters. The experimental results show that the model improves the average accuracy by 14.52%, 17.53%, and 20.71% compared with ResNet18 under the low SNR conditions of −20 dB, −25 dB, and −30 dB, respectively, and the F1 score reaches 94.30%. The study verifies the effectiveness of lightweight design and attention mechanisms in complex acoustic environments.