With the rapid development of Internet technology,the type of information in the Internet is extremely complex,and a large number of riot contents containing bloody,violent and riotous components have appeared.These c...With the rapid development of Internet technology,the type of information in the Internet is extremely complex,and a large number of riot contents containing bloody,violent and riotous components have appeared.These contents pose a great threat to the network ecology and national security.As a result,the importance of monitoring riotous Internet activity cannot be overstated.Convolutional Neural Network(CNN-based)target detection algorithm has great potential in identifying rioters,so this paper focused on the use of improved backbone and optimization function of You Only Look Once v5(YOLOv5),and further optimization of hyperparameters using genetic algorithm to achieve fine-grained recognition of riot image content.First,the fine-grained features of riot-related images were identified,and then the dataset was constructed by manual annotation.Second,the training and testing work was carried out on the constructed dedicated dataset by supervised deep learning training.The research results have shown that the improved YOLOv5 network significantly improved the fine-grained feature extraction capability of riot-related images compared with the original YOLOv5 network structure,and the mean average precision(mAP)value was improved to 0.6128.Thus,it provided strong support for combating riot-related organizations and maintaining the online ecological environment.展开更多
基金This work was supported by Fundamental Research Funds for the Central Universities,People’s Public Security University of China(2021JKF215)Key Projects of the Technology Research Program of the Ministry of Public Security(2021JSZ09)the Fund for the training of top innovative talents to support master’s degree program,People’s Public Security University of china(2021yjsky018).
文摘With the rapid development of Internet technology,the type of information in the Internet is extremely complex,and a large number of riot contents containing bloody,violent and riotous components have appeared.These contents pose a great threat to the network ecology and national security.As a result,the importance of monitoring riotous Internet activity cannot be overstated.Convolutional Neural Network(CNN-based)target detection algorithm has great potential in identifying rioters,so this paper focused on the use of improved backbone and optimization function of You Only Look Once v5(YOLOv5),and further optimization of hyperparameters using genetic algorithm to achieve fine-grained recognition of riot image content.First,the fine-grained features of riot-related images were identified,and then the dataset was constructed by manual annotation.Second,the training and testing work was carried out on the constructed dedicated dataset by supervised deep learning training.The research results have shown that the improved YOLOv5 network significantly improved the fine-grained feature extraction capability of riot-related images compared with the original YOLOv5 network structure,and the mean average precision(mAP)value was improved to 0.6128.Thus,it provided strong support for combating riot-related organizations and maintaining the online ecological environment.
