Hand Gesture Recognition for Real-Time Nano Drone Control on RISC-V
Yexin Zhang, Zhenling Su, Lin Meng · 2025
This paper presents a lightweight convolutional neural network (CNN) model for hand gesture recognition and implements it on a nano drone for flight control. Specifically, the nano drone captures images and determines its flight direction based on the hand gesture recognition results of the captured images. Due to limited hardware resources, the nano drone has only a 64KB L1 cache, a 512KB L2 cache, and a RISC-V CPU, which makes CNN deployment challenging. To address this issue, we design and optimize a 10-layer simplified CNN model and quantize the computations to INT8. Experimental results show that the proposed model achieves 90% accuracy in 5-class hand gesture recognition while maintaining a compact size of only 72.5 KB. Furthermore, it operates at a recognition speed of 7 FPS, enabling real-time performance.