{"title":"ANOMALY DETECTION FOR HERD PIGS BASED ON YOLOX","authors":"Yanwen Li, Juxia Li, Zhenyu Liu, Zhifang Bi, Hui Zhang, Lei Duan","doi":"10.35633/inmateh-69-08","DOIUrl":null,"url":null,"abstract":"In order to solve the problem that the complex pig house environment leads to the difficulty and low accuracy of abnormal detection of group pigs. The video of 9 adult fattening pigs were collected, and the video key frames were obtained by the frame differential method as the training set, and the YOLOX model for abnormal detection of group pigs was constructed. The results show that the average accuracy of YOLOX model on the test set is 98.0%. The research results can provide a reference for the detection of pig anomalies in the breeding environment of pig farms.","PeriodicalId":44197,"journal":{"name":"INMATEH-Agricultural Engineering","volume":" ","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2023-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"INMATEH-Agricultural Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.35633/inmateh-69-08","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
In order to solve the problem that the complex pig house environment leads to the difficulty and low accuracy of abnormal detection of group pigs. The video of 9 adult fattening pigs were collected, and the video key frames were obtained by the frame differential method as the training set, and the YOLOX model for abnormal detection of group pigs was constructed. The results show that the average accuracy of YOLOX model on the test set is 98.0%. The research results can provide a reference for the detection of pig anomalies in the breeding environment of pig farms.
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