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.
MosquitoFusion: A Multiclass Dataset for Real-Time Detection of Mosquitoes, Swarms, and Breeding Sites Using Deep Learning
An integrated computer vision approach using YOLOv8 and GIS achieves high real-time accuracy in detecting mosquitoes, swarms, and breeding sites with MosquitoFusion dataset.
- Year
- 2024
- Venue
- arXiv 2024
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- 3
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2404.01501ARXIV-DEFAULT
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