R. S. Kumar, S. A, A. Balaji, G. Singh, Ashok Kumar, Manikandaprabu P
{"title":"递归CNN模型检测x射线安全图像中的异常检测","authors":"R. S. Kumar, S. A, A. Balaji, G. Singh, Ashok Kumar, Manikandaprabu P","doi":"10.1109/iciptm54933.2022.9754033","DOIUrl":null,"url":null,"abstract":"To address the issue of contraband scale difference in the identification of X-ray pictures during security inspection, we upgrade the Faster RCNN network and propose a multi-channel region proposal network (MCRPN). Multi-layer feature extraction is achieved using the complementarily of distinct levels of convolution features in visual semantics, and the richer semantic components of VGG16 high-level layers and the shallower edge features of low-level layers are fused; To construct a multi-scale contraband detection network, the multi-scale candidate target regions are mapped to the corresponding feature maps; dilated convolutions are introduced into the multi-channel, and a multi-branch dilated convolutions module (DCM) is designed to increase the Receptive field and thus enhance features at different scales. On the self-created data set SIXray OD, the enhanced algorithm achieves an average detection accuracy of 84.69 percent and a test performance improvement of 6.28 percent over the original network. Additionally, the testing findings indicate that the enhanced algorithm's recognition accuracy has been increased to a considerable level.","PeriodicalId":6810,"journal":{"name":"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","volume":"1 1","pages":"742-747"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Recursive CNN Model to Detect Anomaly Detection in X-Ray Security Image\",\"authors\":\"R. S. Kumar, S. A, A. Balaji, G. Singh, Ashok Kumar, Manikandaprabu P\",\"doi\":\"10.1109/iciptm54933.2022.9754033\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To address the issue of contraband scale difference in the identification of X-ray pictures during security inspection, we upgrade the Faster RCNN network and propose a multi-channel region proposal network (MCRPN). Multi-layer feature extraction is achieved using the complementarily of distinct levels of convolution features in visual semantics, and the richer semantic components of VGG16 high-level layers and the shallower edge features of low-level layers are fused; To construct a multi-scale contraband detection network, the multi-scale candidate target regions are mapped to the corresponding feature maps; dilated convolutions are introduced into the multi-channel, and a multi-branch dilated convolutions module (DCM) is designed to increase the Receptive field and thus enhance features at different scales. On the self-created data set SIXray OD, the enhanced algorithm achieves an average detection accuracy of 84.69 percent and a test performance improvement of 6.28 percent over the original network. Additionally, the testing findings indicate that the enhanced algorithm's recognition accuracy has been increased to a considerable level.\",\"PeriodicalId\":6810,\"journal\":{\"name\":\"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)\",\"volume\":\"1 1\",\"pages\":\"742-747\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/iciptm54933.2022.9754033\",\"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 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/iciptm54933.2022.9754033","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Recursive CNN Model to Detect Anomaly Detection in X-Ray Security Image
To address the issue of contraband scale difference in the identification of X-ray pictures during security inspection, we upgrade the Faster RCNN network and propose a multi-channel region proposal network (MCRPN). Multi-layer feature extraction is achieved using the complementarily of distinct levels of convolution features in visual semantics, and the richer semantic components of VGG16 high-level layers and the shallower edge features of low-level layers are fused; To construct a multi-scale contraband detection network, the multi-scale candidate target regions are mapped to the corresponding feature maps; dilated convolutions are introduced into the multi-channel, and a multi-branch dilated convolutions module (DCM) is designed to increase the Receptive field and thus enhance features at different scales. On the self-created data set SIXray OD, the enhanced algorithm achieves an average detection accuracy of 84.69 percent and a test performance improvement of 6.28 percent over the original network. Additionally, the testing findings indicate that the enhanced algorithm's recognition accuracy has been increased to a considerable level.