TakuNet让无人机在应急场景下实时识别图像，功耗低速度飞快。
TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios
- 用深度卷积和早期下采样减少计算量，轻量化设计
- 在两款嵌入式设备上实测超650帧/秒，精度接近顶尖水平
- 适合资源受限的无人机平台，特别适用于紧急救援场景
为嵌入式设备设计高效的神经网络是关键挑战，尤其在需要实时性能的应急响应无人机航拍应用中。本文提出TakuNet，一种新型轻量级架构，采用深度可分离卷积与早期下采样结构降低计算复杂度，同时保持高精度。通过密集连接加速训练收敛，并使用16位浮点精度优化嵌入式硬件加速器上的运行效率。在两个公开数据集上的实验表明，尽管参数量极少，TakuNet在分类应急航空图像任务中仍达到近顶尖准确率。真实环境测试显示，在Jetson Orin Nano（15W）和Raspberry Pi设备上均实现超过650帧/秒的推理速度，证实其在资源受限平台上的高效性，推动无人机在应急场景中的实际应用。代码与实现细节已公开。
原文摘要 · Abstract (English)
Designing efficient neural networks for embedded devices is a critical challenge, particularly in applications requiring real-time performance, such as aerial imaging with drones and UAVs for emergency responses. In this work, we introduce TakuNet, a novel light-weight architecture which employs techniques such as depth-wise convolutions and an early downsampling stem to reduce computational complexity while maintaining high accuracy. It leverages dense connections for fast convergence during training and uses 16-bit floating-point precision for optimization on embedded hardware accelerators. Experimental evaluation on two public datasets shows that TakuNet achieves near-state-of-the-art accuracy in classifying aerial images of emergency situations, despite its minimal parameter count. Real-world tests on embedded devices, namely Jetson Orin Nano and Raspberry Pi, confirm TakuNet's efficiency, achieving more than 650 fps on the 15W Jetson board, making it suitable for real-time AI processing on resource-constrained platforms and advancing the applicability of drones in emergency scenarios. The code and implementation details are publicly released.
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