Juan Ricardo I. Borreta, Justin A. Bautista, A. Yumang
{"title":"使用YOLO识别蜗牛","authors":"Juan Ricardo I. Borreta, Justin A. Bautista, A. Yumang","doi":"10.1109/IICAIET55139.2022.9936736","DOIUrl":null,"url":null,"abstract":"Many species of snails inhabit different areas in the world. Some species have made their way to farmlands and the urban regions, surviving through eating plants and breeding unnoticed making them a cause for concern and a known threat to some crops. A study on snail detection has been previously conducted, but recognizing individual species for their risk has not yet been pursued. This study aims to develop a Tiny-YOLOv4 snail recognition system using a Raspberry Pi. The model focuses on four snail species subject to an input image processed through the system. The outputs show the image with the relevant bounding boxes and labels and notify a user through email for any recognitions. The system produced an overall accuracy of 92%, proving successful in the study's objectives and providing a basis for future literature.","PeriodicalId":142482,"journal":{"name":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Snail Recognition Using YOLO\",\"authors\":\"Juan Ricardo I. Borreta, Justin A. Bautista, A. Yumang\",\"doi\":\"10.1109/IICAIET55139.2022.9936736\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many species of snails inhabit different areas in the world. Some species have made their way to farmlands and the urban regions, surviving through eating plants and breeding unnoticed making them a cause for concern and a known threat to some crops. A study on snail detection has been previously conducted, but recognizing individual species for their risk has not yet been pursued. This study aims to develop a Tiny-YOLOv4 snail recognition system using a Raspberry Pi. The model focuses on four snail species subject to an input image processed through the system. The outputs show the image with the relevant bounding boxes and labels and notify a user through email for any recognitions. The system produced an overall accuracy of 92%, proving successful in the study's objectives and providing a basis for future literature.\",\"PeriodicalId\":142482,\"journal\":{\"name\":\"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)\",\"volume\":\"49 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-09-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IICAIET55139.2022.9936736\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IICAIET55139.2022.9936736","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Many species of snails inhabit different areas in the world. Some species have made their way to farmlands and the urban regions, surviving through eating plants and breeding unnoticed making them a cause for concern and a known threat to some crops. A study on snail detection has been previously conducted, but recognizing individual species for their risk has not yet been pursued. This study aims to develop a Tiny-YOLOv4 snail recognition system using a Raspberry Pi. The model focuses on four snail species subject to an input image processed through the system. The outputs show the image with the relevant bounding boxes and labels and notify a user through email for any recognitions. The system produced an overall accuracy of 92%, proving successful in the study's objectives and providing a basis for future literature.