Embedded AI for Audio-Based Drone Detection in Critical Railway Infrastructure

ENG: Summary. With the increasing threat of unmanned aerial vehicles to critical railway infrastructure, the need for advanced detection technologies has become more urgent. This paper reviews existing railway monitoring solutions and outlines their limitations in identifying aerial threats. An acoustic analysis is conducted to extract distinctive unmanned aerial vehicle sound patterns using Mel-frequency cepstral coefficients, which serve as primary features for classification. Neural network models are applied to detect and differentiate aerial threats from environmental noise, achieving high recognition accuracy. The study also describes the development of an embedded artificial intelligence system based on STM32 microcontrollers, which combines real-time digital signal processing with efficient on-device neural inference. This solution offers a scalable and energy-efficient platform for decentralized audio-based drone detection in railway security applications.