Beibei Zhu, Xiaoyu Wu, Lei Yang, Yinghua Shen, Linglin Wu
{"title":"基于Faster R-CNN的图书自动检测","authors":"Beibei Zhu, Xiaoyu Wu, Lei Yang, Yinghua Shen, Linglin Wu","doi":"10.1109/DIPDMWC.2016.7529355","DOIUrl":null,"url":null,"abstract":"Advances have been made continuously in detection networks such as SPPnet and Fast R-CNN. Recently the novel region proposal method RPN shares full-image convolutional features with the detection network and enables a state-of-the-art object detection network Faster R-CNN. In this work we apply Faster R-CNN to train a detection network on our digital image database of books and implement automatic recognition and positioning of books. Experiments show that retrained Faster R-CNN achieves fine detection results in terms of both speed and accuracy, and it also solves the problem of testing negative examples in our previous study. This provides great help for the study of practical book retrieval system.","PeriodicalId":298218,"journal":{"name":"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"15","resultStr":"{\"title\":\"Automatic detection of books based on Faster R-CNN\",\"authors\":\"Beibei Zhu, Xiaoyu Wu, Lei Yang, Yinghua Shen, Linglin Wu\",\"doi\":\"10.1109/DIPDMWC.2016.7529355\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Advances have been made continuously in detection networks such as SPPnet and Fast R-CNN. Recently the novel region proposal method RPN shares full-image convolutional features with the detection network and enables a state-of-the-art object detection network Faster R-CNN. In this work we apply Faster R-CNN to train a detection network on our digital image database of books and implement automatic recognition and positioning of books. Experiments show that retrained Faster R-CNN achieves fine detection results in terms of both speed and accuracy, and it also solves the problem of testing negative examples in our previous study. This provides great help for the study of practical book retrieval system.\",\"PeriodicalId\":298218,\"journal\":{\"name\":\"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"15\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/DIPDMWC.2016.7529355\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DIPDMWC.2016.7529355","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Automatic detection of books based on Faster R-CNN
Advances have been made continuously in detection networks such as SPPnet and Fast R-CNN. Recently the novel region proposal method RPN shares full-image convolutional features with the detection network and enables a state-of-the-art object detection network Faster R-CNN. In this work we apply Faster R-CNN to train a detection network on our digital image database of books and implement automatic recognition and positioning of books. Experiments show that retrained Faster R-CNN achieves fine detection results in terms of both speed and accuracy, and it also solves the problem of testing negative examples in our previous study. This provides great help for the study of practical book retrieval system.