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Fine Grained Feature Extraction Model of riot-related Images Based on YOLOv5

Computer Systems Science & Engineering 2023年第4期45卷 85-97页

作者： Shaofan Su Deyu Yuan Yuanxin Wang Meng Ding Department of Information Cyber Security People’s Public Security University of ChinaBeijing100038China Key Laboratory of Safety Precautions and Risk Assessment Ministry of Public SecurityBeijing102623China Public Security Behavioral Science Laboratory People’s Public Security University of ChinaBeijing102623China

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 *** contents pose a great threat to the network ecology and national *** a result,the importance of monitoring riotous Internet activity cannot be *** 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 ***,the fine-grained features of riot-related images were identified,and then the dataset was constructed by manual ***,the training and testing work was carried out on the constructed dedicated dataset by supervised deep learning *** 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 ***,it provided strong support for combating riot-related organizations and maintaining the online ecological environment.

关键词： Convolutional neural network YOLOv5 riot-related fine grained target detection

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