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Lightweight Object Detection Using Quantized YOLOv4-Tiny for Emergency Response in Aerial Imagery
- Researchers introduce a lightweight object detection solution using quantized YOLOv4-Tiny for emergency response in aerial imagery.
- The solution targets energy efficiency and effectiveness during emergency situations.
- YOLOv4-Tiny, optimized through post-training quantization to INT8 precision, is the model of choice.
- A custom-curated aerial emergency dataset with 10,820 annotated images was used for training.
- The dataset creation was necessary due to the absence of publicly available drone-view emergency imagery.
- Comparative evaluation against YOLOv5-small was conducted, showcasing metric comparisons such as mAP, F1 score, inference time, and model size.
- The quantized YOLOv4-Tiny demonstrated comparable detection performance, reduced model size from 22.5 MB to 6.4 MB, and boosted inference speed by 44%.
- The model's attributes make it well-suited for real-time emergency detection on low-power edge devices.
- The study contributes a new approach to lightweight object detection in emergency scenarios.
- The methodology emphasizes efficiency without compromising on detection accuracy.
- The custom dataset creation adds value given the unavailability of relevant public datasets.
- Results highlight the efficacy of the quantized YOLOv4-Tiny model for emergency response applications.
- The model's reduced size and improved inference speed enhance its suitability for real-world deployment.
- The approach offers a promising solution for efficient aerial emergency imagery analysis.
- The research findings emphasize the importance of energy-efficient object detection in emergency response contexts.
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