DRNet: A Miniature and Resource- Efficient MAV Detector
Xunkuai Zhou, Bingxin Han, Li Li, Jie Chen, Ben M. Chen · IEEE Transactions on Instrumentation and Measurement · 2025
This article focuses on the challenge of accurate microaerial vehicle (MAV) detection under memory-constrained and lower computational cost conditions. A miniature MAV detection framework, DRNet, is proposed to address this issue. DRNet incorporates a dual skip concatenation (DSC) network and squeezing excitation residual (SER) networks for feature extraction, which enhances the model’s representation capacity. Additionally, spatial attention computation improves object localization and representation capabilities. A lightweight network facilitates efficient feature fusion, further optimizing DRNet’s performance. DRNet’s superior performance over other methods is validated across four challenging datasets. DRNet achieves a comparable accuracy-matching heavyweight method while saving 99.97% parameters, and a compact model size of just 309 kB makes it the smallest high-accuracy, low-computational-requirement MAV detector to date. Furthermore, DRNet can reduce computational costs by 95.3% and GPU memory usage by 50% when processing high-resolution images with dimensions of$1280 \times 1280$. Challenging real-world tests and experimental deployments on edge-computing devices further confirm DRNet’s feasibility and portability.