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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

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.

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

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.
Paper Structure (11 sections, 3 figures, 2 tables)

This paper contains 11 sections, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Existing Works
  3. Methodology
  4. Dataset
  5. Technical Validation
  6. Result Analysis and Future Work
  7. Appendix
  8. Data Collection
  9. Data Preprocessing
  10. Distribution Analysis and Folder Structure
  11. Model Setup and Evaluation

Figures (3)

  • Figure 1: Fully tagged and labeled image. (A), (B), (C), (D) shows the original image. In (A') and (C'), the purple borders mark the breeding sites. In (B') and (D') the yellow borders mark the mosquitos. (D') The red borders mark the swarms
  • Figure 2: Framework of Methodology
  • Figure 3: The folder structure of the MosquitoFusion dataset