MosquitoFusion: A Multiclass Dataset for Real-Time Detection of Mosquitoes, Swarms, and Breeding Sites Using Deep Learning
This work addresses mosquito-borne disease control by automating detection of mosquitoes, swarms, and breeding sites, but it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of real-time mosquito detection by creating a multiclass dataset (MosquitoFusion) with 1204 images and using a pre-trained YOLOv8 model, achieving a mean Average Precision of 57.1% with precision at 73.4% and recall at 50.5%.
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.