A. Amarasinghe, C. Suduwella, Charitha Elvitigala, Lasith Niroshan, Rangana Jayashanka Amaraweera, K. Gunawardana, Prabash Kumarasinghe, K. Zoysa, C. Keppetiyagama
{"title":"A Machine Learning Approach for Identifying Mosquito Breeding Sites via Drone Images","authors":"A. Amarasinghe, C. Suduwella, Charitha Elvitigala, Lasith Niroshan, Rangana Jayashanka Amaraweera, K. Gunawardana, Prabash Kumarasinghe, K. Zoysa, C. Keppetiyagama","doi":"10.1145/3131672.3136986","DOIUrl":null,"url":null,"abstract":"Dengue is one of the deadly and fast spreading diseases in Sri Lanka. The female Aedes mosquito is the dengue vector and these mosquitoes breed in clear and non-flowing water. The Public Health Inspectors (PHIs) are tasked with detecting and eliminating such water collection areas. However, they face the problem of detecting potential breeding sites in hard-to-reach areas. With the technological development, the drones come as one of the most cost effective unmanned vehicles to access the places that a man cannot access. This paper presents a novel approach for identifying mosquito breeding areas via drone images through the distinct coloration of those areas by applying the Histogram of Oriented Gradients (HOG) algorithm. Using the HOG algorithm, we detect potential water retention areas using drone images.","PeriodicalId":424262,"journal":{"name":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3131672.3136986","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15
Abstract
Dengue is one of the deadly and fast spreading diseases in Sri Lanka. The female Aedes mosquito is the dengue vector and these mosquitoes breed in clear and non-flowing water. The Public Health Inspectors (PHIs) are tasked with detecting and eliminating such water collection areas. However, they face the problem of detecting potential breeding sites in hard-to-reach areas. With the technological development, the drones come as one of the most cost effective unmanned vehicles to access the places that a man cannot access. This paper presents a novel approach for identifying mosquito breeding areas via drone images through the distinct coloration of those areas by applying the Histogram of Oriented Gradients (HOG) algorithm. Using the HOG algorithm, we detect potential water retention areas using drone images.
登革热是斯里兰卡致命且传播迅速的疾病之一。雌伊蚊是登革热病媒,这些蚊子在清澈和不流动的水中繁殖。公共卫生检查员的任务是发现和消除这些集水区。然而,他们面临着在难以到达的地区发现潜在繁殖地点的问题。随着技术的发展,无人机成为最具成本效益的无人驾驶工具之一,可以进入人类无法进入的地方。本文提出了一种基于定向梯度直方图(Histogram of Oriented Gradients, HOG)算法,利用无人机图像中不同颜色的区域识别蚊虫孳生区域的新方法。使用HOG算法,我们使用无人机图像检测潜在的水潴留区域。