# TRIDENT: Tri-modal Real-time Intrusion Detection Engine for New Targets

arXiv: https://arxiv.org/abs/2504.06417 · HTML: https://mlsanity.com/paper/2504.06417/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2025.

## Built on by (most cited first, 3 of 3)

- [Uncertainty-Aware Multimodal Anti-UAV Detection via Evidential Fusion and Conflict-Discounted Belief Aggregation](https://mlsanity.com/paper/2608.29235/) (2026)
- [Evidential Deep Learning for Multi-Modal Anti-UAV Detection](https://mlsanity.com/paper/2609.01742/) (2026)
- [Acoustic UAV Detection in Battlefield Scenarios: Handling Noise, Domain Shift, and Weak Labels](https://mlsanity.com/paper/2608.14287/) (2026)

## Abstract (arXiv)

The increasing availability of drones and their potential for malicious activities pose significant privacy and security risks, necessitating fast and reliable detection in real-world environments. However, existing drone detection systems often struggle in real-world settings due to environmental noise and sensor limitations. This paper introduces TRIDENT, a tri-modal drone detection framework that integrates synchronized audio, visual, and RF data to enhance robustness and reduce dependence on individual sensors. TRIDENT introduces two fusion strategies - Late Fusion and GMU Fusion - to improve multi-modal integration while maintaining efficiency. The framework incorporates domain-specific feature extraction techniques alongside a specialized data augmentation pipeline that simulates real-world sensor degradation to improve generalization capabilities. A diverse multi-sensor dataset is collected in urban and non-urban environments under varying lighting conditions, ensuring comprehensive evaluation. Experimental results show that TRIDENT achieves 98.8 percent accuracy in real-world recordings and 83.26 percent in a more complex setting (augmented data), outperforming unimodal and dual-modal baselines. Moreover, TRIDENT operates in real-time, detecting drones in just 6.09 ms while consuming only 75.27 mJ per detection, making it highly efficient for resource-constrained devices. The dataset and code have been released to ensure reproducibility (https://github.com/TRIDENT-2025/TRIDENT).