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

Keywords: Mosquito, Deep Learning, Object Detection, YOLOv8, Dataset, Public Health, Real-time Detection, Computer Vision

TL;DR: This research focuses on advancing mosquito-borne disease prevention through real-time mosquito detection leveraging the MosquitoFusion Dataset that explores innovative solutions for effective disease control.

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.

Submission Number: 79