{"title":"智能社区治理与监测中目标检测的特征向量共享与尺度综合优化","authors":"L. Jianmin","doi":"10.1504/ijcat.2020.10034158","DOIUrl":null,"url":null,"abstract":"This article proposes feature vector sharing and scale comprehensive optimisation strategy of image target detection and recognition method of complex street maximum suppression based on the calculation of the corresponding feature area corresponding to the feature map and completely complete eigenvector. Based on this, this article also combines a fine-tuning method based on transfer learning generalisation, which is suitable for non-convex optimisation and high-dimensional space. First, the method described above implements the optimal rectangular selection box competition based on the scale comprehensive optimisation strategy, and selects the selection box that can reflect the core essence of the target in each classification set. Then, this article realises the model of detecting image target in complex neighbourhood, which improves the accuracy and robustness. Furthermore, we experimentally demonstrate that the accuracy and robustness of our proposed method are superior to those of conventional methods.","PeriodicalId":46624,"journal":{"name":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","volume":"11 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2020-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Feature vector sharing and scale comprehensive optimisation for targets detection in smart neighbourhood governance and monitoring\",\"authors\":\"L. Jianmin\",\"doi\":\"10.1504/ijcat.2020.10034158\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article proposes feature vector sharing and scale comprehensive optimisation strategy of image target detection and recognition method of complex street maximum suppression based on the calculation of the corresponding feature area corresponding to the feature map and completely complete eigenvector. Based on this, this article also combines a fine-tuning method based on transfer learning generalisation, which is suitable for non-convex optimisation and high-dimensional space. First, the method described above implements the optimal rectangular selection box competition based on the scale comprehensive optimisation strategy, and selects the selection box that can reflect the core essence of the target in each classification set. Then, this article realises the model of detecting image target in complex neighbourhood, which improves the accuracy and robustness. Furthermore, we experimentally demonstrate that the accuracy and robustness of our proposed method are superior to those of conventional methods.\",\"PeriodicalId\":46624,\"journal\":{\"name\":\"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2020-12-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1504/ijcat.2020.10034158\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"INTERNATIONAL JOURNAL OF COMPUTER APPLICATIONS IN TECHNOLOGY","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1504/ijcat.2020.10034158","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Feature vector sharing and scale comprehensive optimisation for targets detection in smart neighbourhood governance and monitoring
This article proposes feature vector sharing and scale comprehensive optimisation strategy of image target detection and recognition method of complex street maximum suppression based on the calculation of the corresponding feature area corresponding to the feature map and completely complete eigenvector. Based on this, this article also combines a fine-tuning method based on transfer learning generalisation, which is suitable for non-convex optimisation and high-dimensional space. First, the method described above implements the optimal rectangular selection box competition based on the scale comprehensive optimisation strategy, and selects the selection box that can reflect the core essence of the target in each classification set. Then, this article realises the model of detecting image target in complex neighbourhood, which improves the accuracy and robustness. Furthermore, we experimentally demonstrate that the accuracy and robustness of our proposed method are superior to those of conventional methods.
期刊介绍:
IJCAT addresses issues of computer applications, information and communication systems, software engineering and management, CAD/CAM/CAE, numerical analysis and simulations, finite element methods and analyses, robotics, computer applications in multimedia and new technologies, computer aided learning and training. Topics covered include: -Computer applications in engineering and technology- Computer control system design- CAD/CAM, CAE, CIM and robotics- Computer applications in knowledge-based and expert systems- Computer applications in information technology and communication- Computer-integrated material processing (CIMP)- Computer-aided learning (CAL)- Computer modelling and simulation- Synthetic approach for engineering- Man-machine interface- Software engineering and management- Management techniques and methods- Human computer interaction- Real-time systems