Optical design and the development of high accuracy automated tick classification using computer vision

抽象的。美国蜱传疾病的发病率在过去两年中增加了一倍多 几十年由于缺乏有效的蜱传疾病疫苗， 仍然是减轻疾病的主要重点。蜱虫病媒监测-监测一个区域， 了解蜱种类组成、丰度和空间分布是提供 向处于高风险地区的公众提供准确和最新的信息， 在必要时实现精确的矢量控制。尽管向量的重要性 监督，目前的做法是高度资源密集型的，需要大量的劳动力， 收集和鉴定病媒标本的时间。蜱螨学家或现场分类学家的专业知识是一个 蜱虫识别所需的资源有限，为 国家蜱虫监测实践。虽然移动的应用程序，以方便被动监视 和报告的人蜱遭遇越来越受欢迎，可变的图像质量，有限 参与和科学家错误识别罕见、侵入性或形态相似的蜱虫 物种阻碍了这种方法的可扩展性。没有自动化的解决方案来构建tick 识别能力。我们寻求开发第一个成像和自动识别系统 能够即时准确地识别出美国排名前九的蜱虫媒介。这 该提案将首先描述图像诊断所需的光学要求 与成年蜱相关的形态学特征，并开发标准化的成像平台 用于蜱虫识别。这将有助于开发高质量的蜱虫图像数据集， 与Walter Reed Biosystems Unit（WRBU）合作， 高精度计算机视觉模型用于蜱虫种类和性别识别。最终 这里开发的方法将为实验室和市民提供新的蜱虫识别工具 科学家;允许病媒监测管理人员在实际应用中利用图像识别 这一系统将提高生物监测能力， 科学家们用改进的工具来识别人类与蜱虫相遇时的蜱虫种类。

Abstract. The incidence of US tick-borne diseases has more than doubled in the last two decades. Due to lack of effective vaccines for tick-borne diseases, prevention of tick bites remains the primary focus of disease mitigation. Tick vector surveillance—monitoring an area to understand tick species composition, abundance, and spatial distribution—is key to providing the public with accurate and up-to-date information when they are in areas of high risk, and enabling precision vector control when necessary. Despite the importance of vector surveillance, current practices are highly resource intensive and require significant labor and time to collect and identify vector specimens. Acarologist or field taxonomist expertise is a limited resource required for tick identification, creating a significant capability barrier for national tick surveillance practice. While mobile applications to facilitate passive surveillance and reporting of human-tick encounters have grown in popularity, variable image quality, limited engagement, and scientist misidentification of rare, invasive, or morphologically similar tick species hinder the scalability of this approach. No automated solutions exist to build tick identification capacity. We seek to develop the first imaging and automated identification system capable of instantaneously and accurately identifying the top nine tick vectors in the US. This proposal will first characterize the optical requirements necessary to image diagnostic morphological features associated with adult ticks and develop a standardized imaging platform for tick identification. This will enable the development of a high-quality tick image dataset in partnership with the Walter Reed Biosystems Unit (WRBU) which will be used to train high-accuracy computer vision models for tick species and sex identification. Ultimately the approaches developed here will enable new tick identification tools for both the lab and citizen scientists; allowing vector surveillance managers to leverage image recognition in a practical system that will increase capacity and capability for biosurveillance, and equipping citizen scientists with improved tools to identify tick species during a human-tick encounter.