Cannet A., Simon-chane C., Histace A., Akhoundi M., Romain O., Souchaud M., Jacob P., Sereno D., Boussès Philippe, Sereno Denis. (2024). An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning [Data paper]. Scientific Data, 11 (1), p. 4 [6 p.].
Titre du document
An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning [Data paper]
Année de publication
2024
Auteurs
Cannet A., Simon-chane C., Histace A., Akhoundi M., Romain O., Souchaud M., Jacob P., Sereno D., Boussès Philippe, Sereno Denis
Source
Scientific Data, 2024,
11 (1), p. 4 [6 p.]
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.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Entomologie médicale / Parasitologie / Virologie [052]
Localisation
Fonds IRD [F B010088932]
Identifiant IRD
fdi:010088932
