论文标题
基于深神网络的框架，从图像中提取蚊子的解剖结构
A Framework based on Deep Neural Networks to Extract Anatomy of Mosquitoes from Images
论文作者
论文摘要
我们设计一个基于基于面罩区域的卷积神经网络（Mask R-CNN）的框架，以自动检测并分别从图像中提取蚊子的解剖成分 - 胸腔，翅膀，腹部和腿部。我们的培训数据集由佛罗里达困住的九种蚊子物种的1500张智能手机图像组成。在提出的技术中，第一步是检测蚊子图像中的解剖成分。然后，我们将提取的解剖组件定位和分类，同时将神经网络体系结构中的分支添加到仅包含解剖组件的细分像素中。评估结果是有利的。为了评估普遍性，我们测试仅在大黄蜂图像上使用蚊子图像训练的建筑。我们再次揭示了有利的结果，尤其是在提取机翼时。我们在本文中的技术在公共卫生，分类学和公民科学工作方面具有实际应用。
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.
