EMM-Det: Energy-Efficient Multidrone Tiny Object Detection by Memory-Enhanced Spiking Neural Networks

Le résumé fourni par la source

Unmanned aerial vehicles (UAVs) play a vital role in scenarios such as community safety patrol and disaster search-and-rescue operations due to their maneuverability and deployment flexibility. However, their limited payload capacity, energy constraints, and susceptibility to interference hinder technological advancements. Additionally, centralized training models pose privacy risks, increasing the potential for data leakage. To address these challenges, this article proposes EMM-Det, a low-power, distributed detection method designed for UAV object detection. EMM-Det enhances system performance through three key design strategies: 1) employing memory-enhanced spiking neurons with dynamic leakage constants enhances firing rates and prevents spike decay; 2) utilizing wavelet transform to encode multiscale frequency-domain features improves object detection robustness; and 3) leveraging crowdsourced perception and federated learning (FL) technologies boosts data collection efficiency while mitigating privacy leakage risks. On our constructed dataset, EMM-Det achieves 81.8% mAP@50:95 detection accuracy at extremely low power consumption-3.2% higher than the second-best method and 14.5% superior compared to traditional artificial neural network (ANN) approaches. Experimental results demonstrate that EMM-Det achieves an effective balance between computational efficiency, noise resilience, and data privacy protection. It shows strong potential for deployment in real-world scenarios with stringent energy and privacy requirements, such as community safety patrol and emergency rescue operations.

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