MosquitoFusion: A Multiclass Dataset for Real-Time Detection of Mosquitoes, Swarms, and Breeding Sites Using Deep Learning

Md. Faiyaz Abdullah Sayeedi, Fahim Hafiz, Md Ashiqur Rahman
4/1/2024
cs.CVcs.LG

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Abstract

In this paper, we present an integrated approach to real-time mosquito detection using our multiclass dataset (MosquitoFusion) containing 1204 diverse images and leverage cutting-edge technologies, specifically computer vision, to automate the identification of Mosquitoes, Swarms, and Breeding Sites. The pre-trained YOLOv8 model, trained on this dataset, achieved a mean Average Precision (mAP@50) of 57.1%, with precision at 73.4% and recall at 50.5%. The integration of Geographic Information Systems (GIS) further enriches the depth of our analysis, providing valuable insights into spatial patterns. The dataset and code are available at https://github.com/faiyazabdullah/MosquitoFusion.

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Cite this paper

@article{sayeedi2024mosquitofusion,
  title  = {MosquitoFusion: A Multiclass Dataset for Real-Time Detection of Mosquitoes, Swarms, and Breeding Sites Using Deep Learning},
  author = {Md. Faiyaz Abdullah Sayeedi and Fahim Hafiz and Md Ashiqur Rahman},
  year   = {2024},
  eprint = {2404.01501},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.01501v1}
}

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