{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/trident-tri-modal-real-time-intrusion","title":"TRIDENT: Tri-modal Real-time Intrusion Detection Engine for New Targets","arxiv_id":"2504.06417","date":"2025-04-08","proceeding":null,"authors":["Ildi Alla","Selma Yahia","Valeria Loscri"],"abstract":"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).","url_abs":"https://arxiv.org/abs/2504.06417v2","url_pdf":"https://arxiv.org/pdf/2504.06417v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"trident-tri-modal-real-time-intrusion","repo_url":"https://github.com/trident-2025/trident","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2504.06417","atlas_url":"https://app.syntology.ai/?focus=2504.06417","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}