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Journal Articles Scientific Data Year : 2024

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

Arnaud Cannet
  • Function : Author
Mohammad Akhoundi
  • Function : Author
Marc Souchaud
  • Function : Author
Darian Sereno
  • Function : Author
Philippe Bousses
  • Function : Author
Denis Sereno

Abstract

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.

Dates and versions

hal-04372343 , version 1 (04-01-2024)

Identifiers

Cite

Arnaud Cannet, Camille Simon-Chane, Aymeric Histace, Mohammad Akhoundi, Olivier Romain, et al.. An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning. Scientific Data , 2024, 11 (1), pp.4. ⟨10.1038/s41597-023-02848-y⟩. ⟨hal-04372343⟩
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