{"title":"改进的YOLOv2目标检测模型","authors":"Rui Li, Jun Yang","doi":"10.1109/ICMCS.2018.8525895","DOIUrl":null,"url":null,"abstract":"Aiming at the problem of the large number of model parameters and poor performance on the small-size object of the YOLOv2 object detection model, an improved YOLOv2 object detection model is proposed. Firstly, it improves the YOLOv2 by introducing depth-wise separable convolution replace the standard convolution used in the YOLOv2. The number of parameters on the convolution layer is reduced by 78.83%. Secondly, the Feature Pyramid Network is introduced into the detection model to replace the YOLOv2’s image feature fusion method and perform object detection tasks on multi-scale image features. As a result, the ability of the improved YOLOv2 detection model to detect the small-size object is enhanced. Experimental results on PASCAL VOC 2007 datasets show that the improved YOLOv2 has a competitive accuracy to YOLOv2 and better performance on the small-size object.","PeriodicalId":272255,"journal":{"name":"2018 6th International Conference on Multimedia Computing and Systems (ICMCS)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Improved YOLOv2 Object Detection Model\",\"authors\":\"Rui Li, Jun Yang\",\"doi\":\"10.1109/ICMCS.2018.8525895\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Aiming at the problem of the large number of model parameters and poor performance on the small-size object of the YOLOv2 object detection model, an improved YOLOv2 object detection model is proposed. Firstly, it improves the YOLOv2 by introducing depth-wise separable convolution replace the standard convolution used in the YOLOv2. The number of parameters on the convolution layer is reduced by 78.83%. Secondly, the Feature Pyramid Network is introduced into the detection model to replace the YOLOv2’s image feature fusion method and perform object detection tasks on multi-scale image features. As a result, the ability of the improved YOLOv2 detection model to detect the small-size object is enhanced. Experimental results on PASCAL VOC 2007 datasets show that the improved YOLOv2 has a competitive accuracy to YOLOv2 and better performance on the small-size object.\",\"PeriodicalId\":272255,\"journal\":{\"name\":\"2018 6th International Conference on Multimedia Computing and Systems (ICMCS)\",\"volume\":\"213 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 6th International Conference on Multimedia Computing and Systems (ICMCS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICMCS.2018.8525895\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 6th International Conference on Multimedia Computing and Systems (ICMCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMCS.2018.8525895","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Aiming at the problem of the large number of model parameters and poor performance on the small-size object of the YOLOv2 object detection model, an improved YOLOv2 object detection model is proposed. Firstly, it improves the YOLOv2 by introducing depth-wise separable convolution replace the standard convolution used in the YOLOv2. The number of parameters on the convolution layer is reduced by 78.83%. Secondly, the Feature Pyramid Network is introduced into the detection model to replace the YOLOv2’s image feature fusion method and perform object detection tasks on multi-scale image features. As a result, the ability of the improved YOLOv2 detection model to detect the small-size object is enhanced. Experimental results on PASCAL VOC 2007 datasets show that the improved YOLOv2 has a competitive accuracy to YOLOv2 and better performance on the small-size object.