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annotated 翅干涉模式数据集 医学感兴趣的双翅目昆虫 深度学习。

An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning.

机构信息

Direction des affaires sanitaires et sociales de la Nouvelle-Calédonie, Nouméa, France.

ETIS UMR 8051, Cergy Paris University, ENSEA, CNRS, F-95000, Cergy, France.

出版信息

Sci Data. 2024 Jan 2;11(1):4. doi: 10.1038/s41597-023-02848-y.

DOI:10.1038/s41597-023-02848-y
PMID:38168517
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10761744/
Abstract

Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using WIPs. The dataset includes pictures of Culicidae, Calliphoridae, Muscidae, Tabanidae, Ceratopogonidae, and Psychodidae. The dataset is complemented by previously published datasets of Glossinidae and some Culicidae members. The new dataset contains 2,399 pictures of 18 genera, with each genus documented by a variable number of species and annotated as a class. The dataset covers species variation, with some genera having up to 300 samples.

摘要

已知有几种双翅目昆虫可传播医学和兽医感兴趣的病原体。然而，使用传统方法识别这些物种既费时、费力又昂贵。一种基于计算机视觉的系统可以使用翅干扰模式 (WIP) 来识别这些昆虫，从而解决这个问题。本研究介绍了一个数据集，用于使用 WIP 训练和评估医学和兽医重要的双翅目昆虫识别系统。该数据集包括蚊科、丽蝇科、蝇科、虻科、蠓科和狂蝇科的图片。该数据集还补充了以前发表的鳞翅目和一些蚊科成员的数据集。新数据集包含 2399 张 18 个属的图片，每个属由不同数量的物种记录，并标注为一个类别。该数据集涵盖了物种的变化，有些属有多达 300 个样本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6eed/10761744/b5f69ebda9a6/41597_2023_2848_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6eed/10761744/70177e6a64dd/41597_2023_2848_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6eed/10761744/80a9372de393/41597_2023_2848_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6eed/10761744/b5f69ebda9a6/41597_2023_2848_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6eed/10761744/70177e6a64dd/41597_2023_2848_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6eed/10761744/80a9372de393/41597_2023_2848_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6eed/10761744/b5f69ebda9a6/41597_2023_2848_Fig3_HTML.jpg

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