Lightweight Object Detection Using Quantized YOLOv4-Tiny for Emergency Response in Aerial Imagery
This work addresses the need for lightweight, energy-efficient object detection for emergency responders using aerial imagery, though it is incremental as it applies known quantization techniques to a specific model and dataset.
This paper tackled the problem of real-time object detection in aerial emergency imagery by deploying a quantized YOLOv4-Tiny model, achieving a 71% reduction in model size to 6.4 MB and a 44% improvement in inference speed while maintaining comparable detection performance.
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.