I-Corps: Identification of disease-carrying mosquito vector species using computer vision

I-Corps：利用计算机视觉识别携带疾病的蚊子媒介物种

• 批准号: 2042634
• 负责人: Youseph Yazdi
• 金额: $ 5万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2042634&HistoricalAwards=false
• 关键词: Corps; Identification; disease; carrying; mosquito

The broader impact/commercial potential of this I-Corps project is the development of mosquito vector surveillance methods that enable public health agencies to better evaluate risk of vector-borne disease outbreaks. Surveillance, monitoring, and evaluation of vector populations, such as mosquitoes, is widely recognized as a strategic public health activity that will need to be scaled globally. However, current practices require significant logistical resources and entomological expertise unavailable in many parts of the US and around the world. In the US, nearly half of mosquito control organizations lack the capability and capacity to conduct routine surveillance. This is complicated by varying regional practices, geographies, resources, and local mosquito species compositions. Computer vision is a technical approach that may offer standardized ability to classify different species of mosquitoes. The proposed technology could be embedded in various configurations from lab, field, and remotely deployable devices, reducing surveillance costs while providing data with greater accuracy for decision-making. The flexible nature of this technology may allow it to be modified for additional applications.This I-Corps project is based on the development of a dynamic optics configuration and computer vision system capable of high accuracy classification of mosquito species. Using deep convolutional neural networks (CNN), the platform successfully achieved 94.7% classification accuracy across 24 species of field collected (highly damaged) specimens using computer vision compared with 66% accuracy among entomologists. The optical system optimizes depth of field, color, lighting, and resolution necessary for classification. To increase capacity for mosquito control programs to conduct surveillance, and improve data quality for more effective decision-making, the technology includes automated mosquito traps for remote mosquito classification, a robotic identification and sorting system and a handheld identification and sorting tool.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

I-Corps项目的更广泛影响/商业潜力是开发蚊子病媒监测方法，使公共卫生机构能够更好地评估病媒传播疾病爆发的风险。对媒介种群（如蚊子）的监测、监测和评价被广泛认为是一项需要在全球推广的战略性公共卫生活动。然而，目前的做法需要大量的后勤资源和昆虫学专业知识，这在美国和世界各地的许多地方都是无法获得的。在美国，近一半的蚊虫控制组织缺乏开展常规监测的能力和能力。由于不同的区域做法、地理位置、资源和当地蚊子种类组成，这一问题变得更加复杂。计算机视觉是一种技术方法，可以提供对不同种类蚊子进行分类的标准化能力。所提出的技术可以嵌入到实验室、现场和远程部署设备的各种配置中，降低监控成本，同时为决策提供更准确的数据。这种技术的灵活性允许对其进行修改以适应其他应用。这个I-Corps项目的基础是开发一种动态光学配置和计算机视觉系统，能够对蚊子物种进行高精度分类。利用深度卷积神经网络（CNN），该平台成功地在24种野外采集（高度受损）标本中实现了94.7%的计算机视觉分类准确率，而昆虫学家的准确率为66%。光学系统优化了景深，颜色，照明和分类所需的分辨率。为了提高蚊子控制项目进行监测的能力，并提高数据质量，以便更有效地做出决策，该技术包括用于远程蚊子分类的自动捕蚊器、机器人识别和分类系统以及手持识别和分类工具。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。