TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios

• URL: http://arxiv.org/abs/2501.05880v2
• Date: Thu, 16 Jan 2025 20:35:28 GMT
• Title: TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV systems in Emergency Response Scenarios
• Authors: Daniel Rossi, Guido Borghi, Roberto Vezzani,
• Abstract summary: TakuNet is a novel light-weight architecture which employs techniques such as depth-wise convolutions and an early downsampling stem.<n>It achieves near-state-of-the-art accuracy in classifying aerial images of emergency situations, despite its minimal parameter count.<n>It is suitable for real-time AI processing on resource-constrained platforms and advancing the applicability of drones in emergency scenarios.
• Score: 1.6861784540485294
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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