{"title":"一种基于四个版本的yolo集合的人脸检测方法","authors":"Sanaz Khalili, A. Shakiba","doi":"10.1109/MVIP53647.2022.9738779","DOIUrl":null,"url":null,"abstract":"We implemented a real-time ensemble model for face detection by combining the results of YOLO v1 to v4. We used the WIDER FACE benchmark for training YOLOv1 to v4 in the Darknet framework. Then, we ensemble their results by two methods, namely, WBF (Weighted boxes fusion) and NMW (Non-maximum weighted). The experimental analysis showed that the mAP increases in the WBF ensemble of the models for all the easy, medium, and hard images in the datasets by 7.81%, 22.91%, and 12.96%, respectively. These numbers are 6.25%, 20.83%, and 11.11% for the NMW ensemble.","PeriodicalId":184716,"journal":{"name":"2022 International Conference on Machine Vision and Image Processing (MVIP)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"A face detection method via ensemble of four versions of YOLOs\",\"authors\":\"Sanaz Khalili, A. Shakiba\",\"doi\":\"10.1109/MVIP53647.2022.9738779\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We implemented a real-time ensemble model for face detection by combining the results of YOLO v1 to v4. We used the WIDER FACE benchmark for training YOLOv1 to v4 in the Darknet framework. Then, we ensemble their results by two methods, namely, WBF (Weighted boxes fusion) and NMW (Non-maximum weighted). The experimental analysis showed that the mAP increases in the WBF ensemble of the models for all the easy, medium, and hard images in the datasets by 7.81%, 22.91%, and 12.96%, respectively. These numbers are 6.25%, 20.83%, and 11.11% for the NMW ensemble.\",\"PeriodicalId\":184716,\"journal\":{\"name\":\"2022 International Conference on Machine Vision and Image Processing (MVIP)\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Machine Vision and Image Processing (MVIP)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MVIP53647.2022.9738779\",\"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 International Conference on Machine Vision and Image Processing (MVIP)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MVIP53647.2022.9738779","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A face detection method via ensemble of four versions of YOLOs
We implemented a real-time ensemble model for face detection by combining the results of YOLO v1 to v4. We used the WIDER FACE benchmark for training YOLOv1 to v4 in the Darknet framework. Then, we ensemble their results by two methods, namely, WBF (Weighted boxes fusion) and NMW (Non-maximum weighted). The experimental analysis showed that the mAP increases in the WBF ensemble of the models for all the easy, medium, and hard images in the datasets by 7.81%, 22.91%, and 12.96%, respectively. These numbers are 6.25%, 20.83%, and 11.11% for the NMW ensemble.
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