在树莓派5上实现低功耗实时空中应急目标检测
Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5
- 用TensorFlow Lite对YOLOv4-Tiny进行INT8量化部署
- 每帧推理28.2毫秒，功耗仅13.85瓦，较浮点版显著降低
- 适合资源受限的应急响应场景，如灾后搜救无人机
本文针对资源受限的边缘设备树莓派5，实现了量化后的YOLOv4-Tiny模型在空中应急图像中的实时目标检测。采用TensorFlow Lite后训练量化技术将模型压缩至INT8精度，在嵌入式部署条件下评估了检测速度、功耗与热可行性。量化模型每帧推理时间为28.2毫秒，平均功耗为13.85瓦，相较FP32版本显著降低能耗。在救护车、警车、消防车和车祸等关键应急类别上，检测精度保持稳定。结果表明，该系统具备在安全关键型应急响应任务中实现低功耗实时部署的潜力。
原文摘要 · Abstract (English)
This paper presents the deployment and performance evaluation of a quantized YOLOv4-Tiny model for real-time object detection in aerial emergency imagery on a resource-constrained edge device the Raspberry Pi 5. The YOLOv4-Tiny model was quantized to INT8 precision using TensorFlow Lite post-training quantization techniques and evaluated for detection speed, power consumption, and thermal feasibility under embedded deployment conditions. The quantized model achieved an inference time of 28.2 ms per image with an average power consumption of 13.85 W, demonstrating a significant reduction in power usage compared to its FP32 counterpart. Detection accuracy remained robust across key emergency classes such as Ambulance, Police, Fire Engine, and Car Crash. These results highlight the potential of low-power embedded AI systems for real-time deployment in safety-critical emergency response applications.
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