C. Suduwella, A. Amarasinghe, Lasith Niroshan, Charitha Elvitigala, K. Zoysa, C. Keppetiyagama
{"title":"通过无人机图像识别蚊子滋生地","authors":"C. Suduwella, A. Amarasinghe, Lasith Niroshan, Charitha Elvitigala, K. Zoysa, C. Keppetiyagama","doi":"10.1145/3086439.3086442","DOIUrl":null,"url":null,"abstract":"Public Health Inspectors (PHIs) in Sri Lanka are facing a problem of identifying certain mosquito breeding sites since they cannot easily reach places such as roof gutters, overhead water tanks, inaccessible rooftops and cement materials which are capable of retaining water. The goal of a such inspection of suspected sites is to reduce the number of dengue patients by eradicating dengue mosquito habitats. Due to the retention of water in the aforementioned sites for a long period of time, those places tend to be full of lichens. In general, lichens are visible in dark color. This characteristic helps to identify prolonged water retention areas. With the rapid advancement of technology, the drone has been created as one of the most cost effective apparatus to capture the places that a human cannot access. With respect to the aforesaid context, this paper presents a simple and a novel approach to identify mosquito breeding sites via drone images. The proposed approach processes images captured from a drone to identify possible sites where stagnant water may retain and highlights if such areas are apparent within the image. The evaluation process found that the proposed method, produces a satisfactory level of accuracy in identification of possible water retention areas and the final results depend on the drone camera tilt angle and the effect of shadows.","PeriodicalId":375836,"journal":{"name":"Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"18","resultStr":"{\"title\":\"Identifying Mosquito Breeding Sites via Drone Images\",\"authors\":\"C. Suduwella, A. Amarasinghe, Lasith Niroshan, Charitha Elvitigala, K. Zoysa, C. Keppetiyagama\",\"doi\":\"10.1145/3086439.3086442\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Public Health Inspectors (PHIs) in Sri Lanka are facing a problem of identifying certain mosquito breeding sites since they cannot easily reach places such as roof gutters, overhead water tanks, inaccessible rooftops and cement materials which are capable of retaining water. The goal of a such inspection of suspected sites is to reduce the number of dengue patients by eradicating dengue mosquito habitats. Due to the retention of water in the aforementioned sites for a long period of time, those places tend to be full of lichens. In general, lichens are visible in dark color. This characteristic helps to identify prolonged water retention areas. With the rapid advancement of technology, the drone has been created as one of the most cost effective apparatus to capture the places that a human cannot access. With respect to the aforesaid context, this paper presents a simple and a novel approach to identify mosquito breeding sites via drone images. The proposed approach processes images captured from a drone to identify possible sites where stagnant water may retain and highlights if such areas are apparent within the image. The evaluation process found that the proposed method, produces a satisfactory level of accuracy in identification of possible water retention areas and the final results depend on the drone camera tilt angle and the effect of shadows.\",\"PeriodicalId\":375836,\"journal\":{\"name\":\"Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"18\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3086439.3086442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3086439.3086442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Identifying Mosquito Breeding Sites via Drone Images
Public Health Inspectors (PHIs) in Sri Lanka are facing a problem of identifying certain mosquito breeding sites since they cannot easily reach places such as roof gutters, overhead water tanks, inaccessible rooftops and cement materials which are capable of retaining water. The goal of a such inspection of suspected sites is to reduce the number of dengue patients by eradicating dengue mosquito habitats. Due to the retention of water in the aforementioned sites for a long period of time, those places tend to be full of lichens. In general, lichens are visible in dark color. This characteristic helps to identify prolonged water retention areas. With the rapid advancement of technology, the drone has been created as one of the most cost effective apparatus to capture the places that a human cannot access. With respect to the aforesaid context, this paper presents a simple and a novel approach to identify mosquito breeding sites via drone images. The proposed approach processes images captured from a drone to identify possible sites where stagnant water may retain and highlights if such areas are apparent within the image. The evaluation process found that the proposed method, produces a satisfactory level of accuracy in identification of possible water retention areas and the final results depend on the drone camera tilt angle and the effect of shadows.