用于目标检测的神经网络进行埃及伊蚊虫卵计数
Aedes aegypti Egg Counting with Neural Networks for Object Detection
AI总结：
本研究提出一个包含田间和实验室虫卵的新数据集，并评估三种神经网络（Faster R-CNN、Side-Aware Boundary Localization和FoveaBox）在埃及伊蚊虫卵计数任务上的性能，旨在通过目标检测自动化这一费力且易错的过程。
摘要：
埃及伊蚊仍然是疾病媒介中最主要的问题之一。在众多应对方法中，有一些重要协议利用诱卵器中的虫卵数量来计算指数，如LIRAa和布雷图指数，这些指数可以提供关于可预测的爆发和流行病的信息。此外，许多研究领域需要虫卵数量，特别是在需要大量生产蚊子时。虫卵计数是一项费力且容易出错的任务，可以通过基于计算机视觉的技术实现自动化，特别是基于深度学习的目标检测计数。在这项工作中，我们提出了一个新的数据集，包含田间和实验室虫卵，并给出了三种神经网络在该任务上的测试结果：Faster R-CNN、Side-Aware Boundary Localization和FoveaBox。
英文摘要：
Aedes aegypti is still one of the main concerns when it comes to disease vectors. Among the many ways to deal with it, there are important protocols that make use of egg numbers in ovitraps to calculate indices, such as the LIRAa and the Breteau Index, which can provide information on predictable outbursts and epidemics. Also, there are many research lines that require egg numbers, specially when mass production of mosquitoes is needed. Egg counting is a laborious and error-prone task that can be automated via computer vision-based techniques, specially deep learning-based counting with object detection. In this work, we propose a new dataset comprising field and laboratory eggs, along with test results of three neural networks applied to the task: Faster R-CNN, Side-Aware Boundary Localization and FoveaBox.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
