Automated lab identification and sorting (Rapid-IDX) for mosquito surveillance

概况.拟议的工作将通过以下方式推进最先进的蚊子监测技术 开发了一个自动物种识别和分类系统，Rapid-IDX。该提案建立 在第一阶段的结果，在此期间，机械子组件的隔离，分离， 运输，多角度光学系统，和原型算法的设计和验证。用于相位 第二，所设计的光学和机械系统将被集成到功能原型中， 最小可行产品（MVP），将开发图像数据库，以专注于算法开发 关键的医学相关蚊子物种，迭代功能和可用性测试将 与潜在客户进行沟通。 智力优势。Rapid-IDX将允许高分辨率、多角度图像的快速数据生成 的蚊子标本进行训练的卷积神经网络（CNN）的识别;大型 该技术支持的强大数据集将有助于推动Vectech的技术走向更多 微妙而强大的算法以StarAttGAN为中心的第二阶段工作代表了 关于用于数据集增强的生成模型的技术。这些发展将有助于解决 计算机视觉中的一个关键问题：大量噪声，细粒度，开集多类中的采样偏差 分类问题。 更广泛的影响。拟议的工作支持美国关键的公共卫生基础设施， 面对气候导致的病媒传播疾病发病率不断上升的情况， 变化、全球化和其他因素。该系统将提供病媒控制组织， 保护美国当地社区免受携带疾病的昆虫的侵害， 病媒监测数据收集是病媒综合管理的一个关键组成部分。此外，本发明还 美国武装部队面临着世界各地病媒传播疾病的重大威胁， 美国国防部是病媒监测和控制产品的最大客户之一。我们 在德国、以色列、中国和韩国，害虫信息领域出现了竞争对手 (see商业化计划）。投资Vectech确保有一家美国公司参与竞争 全球市场我们将与美国领先的MCO合作，开发和优化 产品，确保产品与市场的契合度。此外，领导第二阶段提案的技术小组 代表了STEM中几个代表性不足的群体，包括女性，BIPOC和LGBTQ 社区

Overview. The proposed work will advance state of the art mosquito surveillance technology through development of an automated species identification and sorting system, Rapid-IDX. The proposal builds on Phase I results, during which the mechanical subcomponents for isolation, separation, and transportation, multi-angle optics system, and prototype algorithm were designed and validated. For phase II, the optics and mechanical systems designed will be integrated into functional prototypes for a minimum viable product (MVP), an image database will be developed to focus algorithm development on key medically-relevant mosquito species, and iterative functionality and usability testing will be conducted with potential customers. Intellectual Merit. Rapid-IDX will allow for rapid data generation of high-resolution, multi-angle images of mosquito specimens for training of Convolutional Neural Networks (CNNs) for identification; the large and robust dataset enabled with this technology will help push Vectech’s technology towards more nuanced and powerful algorithms. This phase II work centered on StarAttGAN represents the state of the art with regard to generative models for dataset augmentation. These developments will enable addressing a critical issue in computer vision: that of sampling bias in vast noisy, fine-grained, openset multiclass classification problems. Broader Impacts. The proposed work supports United States critical public health infrastructure and public health literacy in the face of growing incidence of vector borne disease as a result of climate change, globalization, and other factors. The system will provide Vector control organizations (VCOs), which protect local communities in the US from disease-carrying insects, with more robust capacity in vector surveillance data gathering, a critical component of integrated vector management. Additionally, US Armed Forces face a significant threat of vector-borne diseases around the world, making the Department of Defense one of the largest customers for vector surveillance and control products. We are seeing emerging competitors in the pest information space in Germany, Israel, China, and South Korea (see commercialization plan). Investment in Vectech ensures there’s an American company to compete in the global market. We will collaborate with leading US-based MCOs for development and optimization of this product, ensuring product-market fit. Furthermore, the technical team leading this phase II proposal represents several underrepresented groups in STEM, including women, BIPOC, and the LGBTQ community.