High accuracy automated tick classification using computer vision

抽象的。在过去的20年里，美国扁虱传播疾病的发病率增加了一倍多。今天, 莱姆病是美国最常见的媒介传播疾病，影响超过 每年有50万美国人。由于缺乏有效的扁虱传播疾病疫苗，预防 预防扁虱叮咬和早期治疗扁虱叮咬是减轻疾病的主要重点。刻度向量 监测-监测一个地区，以了解扁虱的种类组成、丰度和空间分布 分发-是向公众提供准确和最新信息的关键 高风险地区，并在必要时实现精确的病媒控制。尽管向量很重要 目前的做法是高度资源密集型的，需要大量的劳动力和时间来 收集和鉴定病媒标本。考古学家或领域分类学家的专业知识是有限的资源 扁虱识别所需，为国家扁虱监测创造了显著的能力障碍 练习一下。虽然移动应用程序有助于被动监视和报告人类滴答 会面变得越来越受欢迎，图像质量参差不齐，参与度有限，而且是科学家 对稀有的、入侵的或形态相似的扁虱物种的错误识别阻碍了这一方法的可扩展性 接近。到目前为止，还没有自动化的解决方案来建立扁虱识别能力。我们寻求前进 第一阶段工作，成功实现了成像和自动识别系统，能够 即时、准确地识别12种成虫物种，准确率为98%。这项建议 将首先改进第一阶段光学设计以实现可扩展性，以适应更多 若虫、雄性成虫和未吸食或充血的雌性成虫的种内差异。在……里面 同时，我们开发了一些方法来优化移动应用程序中扁虱的引导用户图像质量 面向普通公众的方法。这将使具有代表性的图像数据库的开发成为可能 合作伙伴包括TickSpotters、TickCheck、沃尔特里德生物系统公司(WRBU)和其他公司。这个 生成的数据库将用于训练、验证、测试和部署高精度计算机视觉模型 两款针对专业公共卫生和普通公众的扁虱识别产品。归根结底 这里开发的方法将使病媒管理组织能够利用 一个实用的系统，将增加生物监控的能力和能力，并装备一般 在人类与扁虱的接触中，公众可以使用改进的工具来识别扁虱。

Abstract. The incidence of US tick-borne diseases has more than doubled in the last two decades. Today, Lyme disease is the most common vector-borne disease in the United States, impacting over half-a-million Americans each year. Due to lack of effective vaccines for tick-borne diseases, prevention of tick bites and early tick bite treatment is 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. To date, no automated solutions exist to build tick identification capacity. We seek to advance Phase I work that successfully achieved an imaging and automated identification system capable of instantaneously and accurately identifying twelve adult tick species with 98% accuracy. This proposal will first improve the Phase I optical design for scalability to accommodate imaging of additional intra-specific tick species variability as nymphs, adult males, and unfed or engorged adult females. In parallel, we develop methods to optimize quality of guided user imaging of ticks in a mobile app approach for the general public. This will enable the development of a representative image database with partners including TickSpotters, TickCheck, the Walter Reed Biosystems Unit (WRBU), and others. The resulting database will be used to train, validate, test and deploy high-accuracy computer vision models in two tick identification products for professional public health and the general public. Ultimately the approaches developed here will enable vector management organizations to leverage image recognition in a practical system that will increase capacity and capability for biosurveillance, and equip the general public with improved tools to identify ticks during a human-tick encounter.