An annotated image dataset for training mosquito species recognition system on human skin

• Main Authors: Ong, Song Quan, Hamdan Ahmad
• Format: Article
• Language: English; English
• Published: Springer Nature 2022
• Subjects: QL461-599.82 Insects
• Online Access: https://eprints.ums.edu.my/id/eprint/34493/1/Abstract.pdf; https://eprints.ums.edu.my/id/eprint/34493/2/Full%20text.pdf; https://eprints.ums.edu.my/id/eprint/34493/; https://www.nature.com/articles/s41597-022-01541-w; https://doi.org/10.1038/s41597-022-01541-w

This paper introduces a new mosquito images dataset that is suitable for training and evaluating a recognition system on mosquitoes in normal or smashed conditions. The images dataset served mainly for the development a machine learning model that can recognize the mosquito in the public community, which commonly found in the smashed/damaged form by human. Especially the images of mosquito in hashed condition, which to the best of our knowledge, a dataset that fulfilled such condition is not available. There are three mosquito species in the dataset, which are Aedes aegypti, Aedes albopictus and Culex quinquefasciatus, and the images were annotated until species level due to the specimen was purely bred in a WHO accredited breeding laboratory. The dataset consists of seven root fles, six root fles that composed of six classes (each species with either normal landing, or random damaged conditions) with a total of 1500 images, and one pre-processed fle which consists of a train, test and prediction set, respectively for model construction.