A Framework based on Deep Neural Networks to Extract Anatomy of Mosquitoes from Images
This work addresses the problem of automating mosquito anatomy extraction for applications in public health, taxonomy, and citizen science, but it is incremental as it applies an existing method to a new domain.
The authors developed a Mask R-CNN-based framework to automatically detect and segment anatomical components of mosquitoes from images, achieving favorable results on a dataset of 1500 images across nine species and showing generality by testing on bumblebee images.
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.