A Framework based on Deep Neural Networks to Extract Anatomy of Mosquitoes from Images

TL;DR

This paper presents a deep learning framework using Mask R-CNN to automatically detect, classify, and segment mosquito anatomical parts from images, aiding public health and scientific research.

Contribution

It introduces a novel application of Mask R-CNN for mosquito anatomy extraction, including a multi-task architecture for detection, classification, and segmentation.

Findings

High accuracy in detecting mosquito anatomical components

Successful generalization to bumblebee images

Practical applications in health and taxonomy

Abstract

We design a framework based on Mask Region-based Convolutional Neural Network (Mask R-CNN) to automatically detect and separately extract anatomical components of mosquitoes - thorax, wings, abdomen and legs from images. Our training dataset consisted of 1500 smartphone images of nine mosquito species trapped in Florida. In the proposed technique, the first step is to detect anatomical components within a mosquito image. Then, we localize and classify the extracted anatomical components, while simultaneously adding a branch in the neural network architecture to segment pixels containing only the anatomical components. Evaluation results are favorable. To evaluate generality, we test our architecture trained only with mosquito images on bumblebee images. We again reveal favorable results, particularly in extracting wings. Our techniques in this paper have practical applications in public health, taxonomy and citizen-science efforts.