DroneAVFusion: Towards Drone-Incursion Detection and Classification Using Acoustic–Visual Fusion

The rapid development of small unmanned aerial vehicles (UAVs) has introduced significant challenges in both civilian and defense security domains, such as public safety and protecting critical infrastructure. Detecting and classifying misused drones intruding into low-altitude, cluttered environments remains particularly challenging because of limitations in robust fusion across multiple sensing modalities. Although acoustic and visual fusion has been widely studied, its use for detecting intruding drones has not been sufficiently investigated. This paper proposes DroneAVFusion, a lightweight acoustic–visual fusion pipeline that integrates acoustic and visual sensing via a novel Late Attention Fusion (LAF) mechanism. The proposed system combines a Mel-spectrogram-based convolutional neural network for acoustic detection and optimized YOLO-based visual detection, followed by a computationally efficient fusion module that dynamically weights modalities using confidence-aware gating and temperature-scaled attention. A custom acoustic–visual dataset comprising three commercial drones and non-drone objects is collected and used to evaluate performance under realistic conditions. Experimental results show that DroneAVFusion achieves a 99.6% F1 score on the validation set and perfect performance on the test set while running at an average of 30.40 FPS with only 72,868 parameters in the fusion module. Compared with single-modality baselines and existing fusion approaches, the proposed method demonstrates excellent robustness, making it well-suited for security applications.