Summary of Mosquitofusion: a Multiclass Dataset For Real-time Detection Of Mosquitoes, Swarms, and Breeding Sites Using Deep Learning, by Md. Faiyaz Abdullah Sayeedi et al.
MosquitoFusion: A Multiclass Dataset for Real-Time Detection of Mosquitoes, Swarms, and Breeding Sites Using Deep Learning
by Md. Faiyaz Abdullah Sayeedi, Fahim Hafiz, Md Ashiqur Rahman
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel approach to real-time mosquito detection using computer vision and deep learning. The researchers created a multiclass dataset called MosquitoFusion, comprising 1204 diverse images of mosquitoes, swarms, and breeding sites. They trained the YOLOv8 model on this dataset, achieving 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) adds spatial insights to the analysis. This work has implications for disease surveillance and control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to quickly find mosquitoes using computer programs. Scientists made a big collection of pictures of mosquitoes, groups of mosquitoes, and where they breed. They used this collection to train a special computer model that can identify mosquitoes in real-time. The model was very good at finding mosquitoes, with 57% accuracy. By adding maps and spatial information, the researchers could see patterns and trends about where mosquitoes are found. This work helps us understand how to track and control diseases. |
Keywords
» Artificial intelligence » Deep learning » Mean average precision » Precision » Recall
