量化YOLOv4-Tiny实现无人机应急图像实时检测，模型更小更快
Lightweight Object Detection Using Quantized YOLOv4-Tiny for Emergency Response in Aerial Imagery
- 用后训练量化将YOLOv4-Tiny压缩至INT8精度
- 模型大小从22.5MB减至6.4MB，推理速度提升44%
- 专为应急场景设计的10820张航拍图数据集
本文提出一种轻量级、低功耗的空中应急影像目标检测方案。针对应急响应中的无人机影像，采用紧凑型卷积神经网络YOLOv4-Tiny，并通过后训练量化优化至INT8精度。模型在自建的10,820张标注的航拍应急图像数据集上训练，该数据集因缺乏公开可用的无人机视角应急影像而具有重要价值。与YOLOv5-small对比，量化后的YOLOv4-Tiny在平均精度（mAP）、F1分数、推理时间及模型大小等指标上表现优异：模型尺寸由22.5MB降至6.4MB，推理速度提升44%。71%的模型体积缩减和44%的加速使其非常适合部署于低功耗边缘设备，实现实时应急目标检测。
原文摘要 · Abstract (English)
This paper presents a lightweight and energy-efficient object detection solution for aerial imagery captured during emergency response situations. We focus on deploying the YOLOv4-Tiny model, a compact convolutional neural network, optimized through post-training quantization to INT8 precision. The model is trained on a custom-curated aerial emergency dataset, consisting of 10,820 annotated images covering critical emergency scenarios. Unlike prior works that rely on publicly available datasets, we created this dataset ourselves due to the lack of publicly available drone-view emergency imagery, making the dataset itself a key contribution of this work. The quantized model is evaluated against YOLOv5-small across multiple metrics, including mean Average Precision (mAP), F1 score, inference time, and model size. Experimental results demonstrate that the quantized YOLOv4-Tiny achieves comparable detection performance while reducing the model size from 22.5 MB to 6.4 MB and improving inference speed by 44\%. With a 71\% reduction in model size and a 44\% increase in inference speed, the quantized YOLOv4-Tiny model proves highly suitable for real-time emergency detection on low-power edge devices.
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