Lightweight Object Detection Using Quantized YOLOv4-Tiny for Emergency Response in Aerial Imagery

• URL: http://arxiv.org/abs/2506.09299v1
• Date: Tue, 10 Jun 2025 23:26:29 GMT
• Title: Lightweight Object Detection Using Quantized YOLOv4-Tiny for Emergency Response in Aerial Imagery
• Authors: Sindhu Boddu, Arindam Mukherjee,
• Abstract summary: We focus on deploying the YOLOv4-Tiny model, a compact convolutional neural network, optimized through post-training quantization to INT8 precision.<n>The model is trained on a custom-curated aerial emergency dataset, consisting of 10,820 annotated images covering critical emergency scenarios.<n>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.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• Efficient Edge Deployment of Quantized YOLOv4-Tiny for Aerial Emergency Object Detection on Raspberry Pi 5 [0.0]
  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 Raspberry Pi 5.<n>The 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.
  arXiv  Detail & Related papers  (2025-06-10T23:33:20Z)
• A lightweight model FDM-YOLO for small target improvement based on YOLOv8 [0.0]
  Small targets are difficult to detect due to their low pixel count, complex backgrounds, and varying shooting angles.<n>This paper focuses on small target detection and explores methods for object detection under low computational constraints.
  arXiv  Detail & Related papers  (2025-03-06T14:06:35Z)
• YOLO-ELA: Efficient Local Attention Modeling for High-Performance Real-Time Insulator Defect Detection [0.0]
  Existing detection methods for insulator defect identification from unmanned aerial vehicles struggle with complex background scenes and small objects. This paper proposes a new attention-based foundation architecture, YOLO-ELA, to address this issue. Experimental results on high-resolution UAV images show that our method achieved a state-of-the-art performance of 96.9% mAP0.5 and a real-time detection speed of 74.63 frames per second.
  arXiv  Detail & Related papers  (2024-10-15T16:00:01Z)
• QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning [52.157939524815866]
  In this paper, we empirically unravel three properties in quantized diffusion models that compromise the efficacy of current methods. We identify two critical types of quantized layers: those holding vital temporal information and those sensitive to reduced bit-width. Our method is evaluated over three high-resolution image generation tasks and achieves state-of-the-art performance under various bit-width settings.
  arXiv  Detail & Related papers  (2024-02-06T03:39:44Z)
• Diffusion-Based Particle-DETR for BEV Perception [94.88305708174796]
  Bird-Eye-View (BEV) is one of the most widely-used scene representations for visual perception in Autonomous Vehicles (AVs) Recent diffusion-based methods offer a promising approach to uncertainty modeling for visual perception but fail to effectively detect small objects in the large coverage of the BEV. Here, we address this problem by combining the diffusion paradigm with current state-of-the-art 3D object detectors in BEV.
  arXiv  Detail & Related papers  (2023-12-18T09:52:14Z)
• Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection [55.2480439325792]
  We present an in-depth evaluation of an object detection model that integrates the LSKNet backbone with the DiffusionDet head. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis.
  arXiv  Detail & Related papers  (2023-11-21T19:49:13Z)
• QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection [57.019527599167255]
  Multi-view 3D detection based on BEV (bird-eye-view) has recently achieved significant improvements. We show in our paper that directly applying quantization in BEV tasks will 1) make the training unstable, and 2) lead to intolerable performance degradation. Our method QD-BEV enables a novel view-guided distillation (VGD) objective, which can stabilize the quantization-aware training (QAT) while enhancing the model performance.
  arXiv  Detail & Related papers  (2023-08-21T07:06:49Z)
• QLoRA: Efficient Finetuning of Quantized LLMs [66.58009990713134]
  We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU. QLoRA backpropagates through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters(LoRA) Our best model family, which we name Guanaco, outperforms all previous openly released models on the Vicuna benchmark.
  arXiv  Detail & Related papers  (2023-05-23T17:50:33Z)
• Real-Time Flying Object Detection with YOLOv8 [0.0]
  This paper presents a generalized model for real-time detection of flying objects. We also present a refined model that achieves state-of-the-art results for flying object detection.
  arXiv  Detail & Related papers  (2023-05-17T06:11:10Z)
• Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras [62.70541164894224]
  We introduce several architecture choices which allow us to scale the depth and complexity of such models while maintaining low computation. Our method runs 3.7 times faster than a dense graph neural network, taking only 8.4 ms per forward pass.
  arXiv  Detail & Related papers  (2022-11-22T15:14:20Z)
• Channel Pruned YOLOv5-based Deep Learning Approach for Rapid and Accurate Outdoor Obstacles Detection [6.703770367794502]
  One-stage algorithm have been widely used in target detection systems that need to be trained with massive data. Due to their convolutional structure, they need more computing power and greater memory consumption. We apply pruning strategy to target detection networks to reduce the number of parameters and the size of model.
  arXiv  Detail & Related papers  (2022-04-27T21:06:04Z)
• Analysis and Adaptation of YOLOv4 for Object Detection in Aerial Images [0.0]
  Our work shows the adaptation of the popular YOLOv4 framework for predicting the objects and their locations in aerial images. The trained model resulted in a mean average precision (mAP) of 45.64% with an inference speed reaching 8.7 FPS on the Tesla K80 GPU. A comparative study with several contemporary aerial object detectors proved that YOLOv4 performed better, implying a more suitable detection algorithm to incorporate on aerial platforms.
  arXiv  Detail & Related papers  (2022-03-18T23:51:09Z)
• Probing Model Signal-Awareness via Prediction-Preserving Input Minimization [67.62847721118142]
  We evaluate models' ability to capture the correct vulnerability signals to produce their predictions. We measure the signal awareness of models using a new metric we propose- Signal-aware Recall (SAR) The results show a sharp drop in the model's Recall from the high 90s to sub-60s with the new metric.
  arXiv  Detail & Related papers  (2020-11-25T20:05:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.