aiqbench analysis
This research presents a lightweight convolutional neural network designed to detect drone RF emissions directly from time-domain signals without frequency preprocessing. By optimizing model size and inference speed for embedded GNU Radio systems, the study addresses critical needs in electronic warfare scenarios requiring real-time processing on resource-constrained hardware. The approach demonstrates that eliminating traditional spectrogram steps significantly reduces computational costs while maintaining high detection accuracy.
Compact convolutional neural networks for AI-based drone detection system
arXiv:2607.16455v1 Announce Type: new Abstract: The increasing use of first-person-view drones in modern conflicts has created a demand for compact and reliable detection systems capable of operating in complex electromagnetic environments. These drones continuously transmit video signals through onboard video transmitters, generating radio-frequency emissions that can be exploited for early detection. This study investigates the use of lightweight convolutional neural networks for automated detection of drone signals captured by a software-defined radio-based electronic warfare framework. Samples are converted into rasterized time-domain images, providing a computationally efficient input representation suitable for embedded systems. Several custom model architectures were designed and benchmarked in terms of accuracy, model size, and inference performance using a dataset containing approximately 40,000 labeled images. In addition to offline testing, the models were integrated into a GNU Radio signal processing chain for real-time evaluation. The results show that compact models can achieve high detection accuracy while maintaining low computational requirements, making them suitable for embedded radio-frequency monitoring applications. Compared with existing spectrogram-based RF detection methods, the proposed approach eliminates frequency-domain preprocessing and achieves comparable accuracy with significantly reduced computational cost.
Abstract:The increasing use of first-person-view drones in modern conflicts has created a demand for compact and reliable detection systems capable of operating in complex electromagnetic environments. These drones continuously transmit video signals through onboard video transmitters, generating radio-frequency emissions that can be exploited for early detection. This study investigates the use of lightweight convolutional neural networks for automated detection of drone signals captured by a software-defined radio-based electronic warfare framework. Samples are converted into rasterized time-domain images, providing a computationally efficient input representation suitable for embedded systems. Several custom model architectures were designed and benchmarked in terms of accuracy, model size, and inference performance using a dataset containing approximately 40,000 labeled images. In addition to offline testing, the models were integrated into a GNU Radio signal processing chain for real-time evaluation. The results show that compact models can achieve high detection accuracy while maintaining low computational requirements, making them suitable for embedded radio-frequency monitoring applications. Compared with existing spectrogram-based RF detection methods, the proposed approach eliminates frequency-domain preprocessing and achieves comparable accuracy with significantly reduced computational cost.
Machine Learning (cs.LG)
arXiv:2607.16455 [cs.LG]
(or arXiv:2607.16455v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.16455 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 271 words
Related coverage
Graph neural networks at war: integrating cybersecurity and drone intelligence in the Israeli-Iranian conflict
arXiv · Jun 17, 2026
News
Quantum Computing Report · Jul 25, 2026
Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
arXiv · Sep 10, 2026
Links #5: 2026/07
LessWrong · Jul 31, 2026