Mobile Phones Hematophagous Diptera Surveillance in the field using Deep Learning and Wing Interference Patterns
Résumé
Real-time monitoring of hematophagous diptera
(such as mosquitoes) populations in the field is a crucial challenge
to foresee vaccination campaigns and to restrain potential diseases
spreading. However, current methods heavily rely on costly
DNA extraction which is destructive, costly, time consuming and
requires experts. The contributions of this work are: 1) the usage
of a new type of imaging, named Wing Interference Patterns
(WIPs), which is non-destructive and easier to produce during
in the field experiments; 2) a deep learning architecture which
is optimized for very low computation cost, memory usage and
a short inference time; 3) the use of a dataset of more than
50 medically important species of hematophagous diptera with
more than 3000 images of WIPs. With these contributions, we
demonstrate that WIPs are an excellent medium to automatically
recognize a large amount of hematophagous diptera species with
very high accuracy and low computational cost convolutional
neural network.