Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13858
Han Jiang, Hequn Xian
In recent times, the issue of vehicle location privacy has received increasing attention. Location-based services (LBSs) require users’ location information to be constantly updated to service providers, which causes the location information to be speculated and attacked by malicious entities. The pseudonym schemes offer a viable solution to the aforementioned problem, but existing pseudonym schemes do not provide differentiated protection for users’ varying locations, thereby increasing the possibility of location information leakage. To address this concern, we propose a sensitivity-based pseudonym exchange mechanism, which leverages the vehicle’s historical track record to extract features and enable customized location privacy protection. Performance evaluation results demonstrate that our approach significantly outperforms existing approaches in achieving location privacy.
{"title":"A Sensitivity-based Location Privacy Protection Scheme in Vehicular Networks","authors":"Han Jiang, Hequn Xian","doi":"10.54097/fcis.v5i3.13858","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13858","url":null,"abstract":"In recent times, the issue of vehicle location privacy has received increasing attention. Location-based services (LBSs) require users’ location information to be constantly updated to service providers, which causes the location information to be speculated and attacked by malicious entities. The pseudonym schemes offer a viable solution to the aforementioned problem, but existing pseudonym schemes do not provide differentiated protection for users’ varying locations, thereby increasing the possibility of location information leakage. To address this concern, we propose a sensitivity-based pseudonym exchange mechanism, which leverages the vehicle’s historical track record to extract features and enable customized location privacy protection. Performance evaluation results demonstrate that our approach significantly outperforms existing approaches in achieving location privacy.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"15 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13859
Wendong Yang
Presently, humanity has entered the era of big data, where artificial intelligence (AI) technology has found extensive application across various industries and domains. Notably, in the field of computer network technology, its utilization has significantly elevated the technological prowess of computer science, propelling computer systems towards a gradual trajectory of stability and intelligence. Consequently, it has transformed into an indispensable tool in people's daily lives. This paper commences by expounding on the contemporary concept and characteristics of artificial intelligence technology, followed by a synthesis of the advantages of its implementation in the realm of computer network technology based on relevant literature. Furthermore, practical demonstrations are provided to illustrate the efficacy of artificial intelligence in the domain of cloud computing.
{"title":"The Advantages of Artificial Intelligence Application in Computer Technology","authors":"Wendong Yang","doi":"10.54097/fcis.v5i3.13859","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13859","url":null,"abstract":"Presently, humanity has entered the era of big data, where artificial intelligence (AI) technology has found extensive application across various industries and domains. Notably, in the field of computer network technology, its utilization has significantly elevated the technological prowess of computer science, propelling computer systems towards a gradual trajectory of stability and intelligence. Consequently, it has transformed into an indispensable tool in people's daily lives. This paper commences by expounding on the contemporary concept and characteristics of artificial intelligence technology, followed by a synthesis of the advantages of its implementation in the realm of computer network technology based on relevant literature. Furthermore, practical demonstrations are provided to illustrate the efficacy of artificial intelligence in the domain of cloud computing.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"17 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13848
Songling Huang, Zhenji Wen, Hanling Li
In this study, a method of arrhythmia classification and risk prediction based on deep neural network and convolutional neural network (CNN) is proposed for ECG data. Electrocardiogram data record the electrophysiological activity of the heart, including normal heart beats and various arrhythmias. In order to monitor and identify arrhythmia in real time and accurately, this study used CNN model for data analysis. The characteristics of CNN, such as local perception, parameter sharing and multi-level feature extraction, make it perform well in ECG data analysis. The data comes from the ' Certification Cup ' Mathematics China Mathematical Modeling Network Challenge in 2023 and is preprocessed to meet the needs of the model. In the process of establishing and solving the model, the cross-entropy loss function is used to optimize, and the effectiveness and robustness of the model are verified by various evaluation methods. The results show that the model can accurately classify and predict the risk of arrhythmia, providing a powerful diagnostic tool for doctors and a valuable reference for future arrhythmia research.
{"title":"Research on Arrhythmia Classification and Risk Degree Prediction based on Deep Neural Network and Convolutional Neural Network","authors":"Songling Huang, Zhenji Wen, Hanling Li","doi":"10.54097/fcis.v5i3.13848","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13848","url":null,"abstract":"In this study, a method of arrhythmia classification and risk prediction based on deep neural network and convolutional neural network (CNN) is proposed for ECG data. Electrocardiogram data record the electrophysiological activity of the heart, including normal heart beats and various arrhythmias. In order to monitor and identify arrhythmia in real time and accurately, this study used CNN model for data analysis. The characteristics of CNN, such as local perception, parameter sharing and multi-level feature extraction, make it perform well in ECG data analysis. The data comes from the ' Certification Cup ' Mathematics China Mathematical Modeling Network Challenge in 2023 and is preprocessed to meet the needs of the model. In the process of establishing and solving the model, the cross-entropy loss function is used to optimize, and the effectiveness and robustness of the model are verified by various evaluation methods. The results show that the model can accurately classify and predict the risk of arrhythmia, providing a powerful diagnostic tool for doctors and a valuable reference for future arrhythmia research.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13862
Liming Zhou
This paper designs an automatic control smart trash can based on STM32 microcontroller, which mainly realizes the functions of garbage identification and classification, overflowing garbage reminder, automatic opening and closing of the garbage can lid, and short-distance remote control. The automatic control smart trash can not only helps the user to recognize the type of trash automatically, freeing the user from a wide variety of trash categories. Since all garbage contains a lot of bacteria, the sensor recognizes the human body to automatically open and close the garbage lid also protects human health to a certain extent. Automatic control of smart trash cans not only provides convenience for the users, but also benefits the garbage removers. It can open all the lids of the trash cans with one click when the garbage is full, which also further reduces the workload of the garbage removers.
{"title":"The Design of the Automatic Control Intelligent Ashbin","authors":"Liming Zhou","doi":"10.54097/fcis.v5i3.13862","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13862","url":null,"abstract":"This paper designs an automatic control smart trash can based on STM32 microcontroller, which mainly realizes the functions of garbage identification and classification, overflowing garbage reminder, automatic opening and closing of the garbage can lid, and short-distance remote control. The automatic control smart trash can not only helps the user to recognize the type of trash automatically, freeing the user from a wide variety of trash categories. Since all garbage contains a lot of bacteria, the sensor recognizes the human body to automatically open and close the garbage lid also protects human health to a certain extent. Automatic control of smart trash cans not only provides convenience for the users, but also benefits the garbage removers. It can open all the lids of the trash cans with one click when the garbage is full, which also further reduces the workload of the garbage removers.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276424","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13836
Zepeng Shen
With the rapid development of artificial intelligence, data visualization analysis platforms have been widely applied in various fields. This study mainly explores the optimization and performance improvement of algorithms for data visualization analysis platforms based on artificial intelligence. Firstly, the definition of a data visualization analysis platform and the application of artificial intelligence in it were introduced, and the current problems and challenges were pointed out. Then, a discussion was conducted on algorithm optimization for various stages of research, including data preprocessing, data clustering, data classification, and optimization of data association analysis algorithms. Subsequently, the study proposed some performance improvement methods, including the application of parallel computing technology, distributed computing technology, data compression technology, and data indexing technology.
{"title":"Algorithm Optimization and Performance Improvement of Data Visualization Analysis Platform based on Artificial Intelligence","authors":"Zepeng Shen","doi":"10.54097/fcis.v5i3.13836","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13836","url":null,"abstract":"With the rapid development of artificial intelligence, data visualization analysis platforms have been widely applied in various fields. This study mainly explores the optimization and performance improvement of algorithms for data visualization analysis platforms based on artificial intelligence. Firstly, the definition of a data visualization analysis platform and the application of artificial intelligence in it were introduced, and the current problems and challenges were pointed out. Then, a discussion was conducted on algorithm optimization for various stages of research, including data preprocessing, data clustering, data classification, and optimization of data association analysis algorithms. Subsequently, the study proposed some performance improvement methods, including the application of parallel computing technology, distributed computing technology, data compression technology, and data indexing technology.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"61 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13839
Ru Zhang, Zhenhua Guo, Yangfan Xu
The style of ancient ceramics has always been one of the most important factors affecting the sales of antique ceramic products. Although it has certain rules, due to the complexity of current product design and the limitations of designers in the design process, intelligent algorithms are needed to assist in design. In response to the diversity of intelligent design solutions for antique ceramic products, this paper proposes a research method for antique ceramic product design based on AGCGAN. Combining with the universal links in the process of intelligent product design, a generative adversarial network is used to learn the rules of excellent product design samples, and the generator generation scheme is obtained. Then, through the constructed design scheme filter, the generation scheme is filtered according to the requirements, and a design scheme generation system with certain reference value is constructed.
{"title":"Design of an Intelligent Algorithm for Rural Antique Ceramic Bottom Pattern","authors":"Ru Zhang, Zhenhua Guo, Yangfan Xu","doi":"10.54097/fcis.v5i3.13839","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13839","url":null,"abstract":"The style of ancient ceramics has always been one of the most important factors affecting the sales of antique ceramic products. Although it has certain rules, due to the complexity of current product design and the limitations of designers in the design process, intelligent algorithms are needed to assist in design. In response to the diversity of intelligent design solutions for antique ceramic products, this paper proposes a research method for antique ceramic product design based on AGCGAN. Combining with the universal links in the process of intelligent product design, a generative adversarial network is used to learn the rules of excellent product design samples, and the generator generation scheme is obtained. Then, through the constructed design scheme filter, the generation scheme is filtered according to the requirements, and a design scheme generation system with certain reference value is constructed.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"19 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139277695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13814
Ying Feng, Yongqin Wang
Intelligent diagnosis is an important scenario in smart healthcare, with conversational diagnostic scenarios being the most common. The process of collecting symptom information through conversations with users and inferring diseases based on symptoms. Through a dialogue based diagnostic system, it can meet some of the medical consultation needs of residents, thereby freeing doctors from some basic consultations and greatly alleviating the shortage of medical resources. In the actual diagnosis process, the symptoms reported by patients are often insufficient to support accurate diagnosis. It is necessary to ask the user if they have any other symptoms through dialogue to form a diagnostic conclusion. Existing research mainly adopts reinforcement learning methods, which gradually learn the dialogue process between traditional Chinese medicine students and patients in real medical scenarios, and obtain strategies for symptom inquiry and disease diagnosis. Despite the advantages of reinforcement learning in dealing with temporal decision problems, the diagnostic accuracy is still low and data dependency is strong. In this article, a medical dialogue robot architecture based on medical dialogue diagnosis technology, medical knowledge graph technology, and "inference machine" technology is proposed to build an intelligent diagnosis architecture. Secondly, in terms of algorithm, this article proposes a disease diagnosis algorithm based on Naive Bayes Classification and a symptom screening algorithm based on symptom set differences for symptom query process, This algorithm increases the interpretability of diagnostic results by simulating the questioning and diagnostic process of doctors, and combines it with the medical dialogue robot architecture to achieve intelligent diagnosis throughout the entire process.
{"title":"Intelligent Diagnostic System Development based on Artificial Intelligence Technology","authors":"Ying Feng, Yongqin Wang","doi":"10.54097/fcis.v5i3.13814","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13814","url":null,"abstract":"Intelligent diagnosis is an important scenario in smart healthcare, with conversational diagnostic scenarios being the most common. The process of collecting symptom information through conversations with users and inferring diseases based on symptoms. Through a dialogue based diagnostic system, it can meet some of the medical consultation needs of residents, thereby freeing doctors from some basic consultations and greatly alleviating the shortage of medical resources. In the actual diagnosis process, the symptoms reported by patients are often insufficient to support accurate diagnosis. It is necessary to ask the user if they have any other symptoms through dialogue to form a diagnostic conclusion. Existing research mainly adopts reinforcement learning methods, which gradually learn the dialogue process between traditional Chinese medicine students and patients in real medical scenarios, and obtain strategies for symptom inquiry and disease diagnosis. Despite the advantages of reinforcement learning in dealing with temporal decision problems, the diagnostic accuracy is still low and data dependency is strong. In this article, a medical dialogue robot architecture based on medical dialogue diagnosis technology, medical knowledge graph technology, and \"inference machine\" technology is proposed to build an intelligent diagnosis architecture. Secondly, in terms of algorithm, this article proposes a disease diagnosis algorithm based on Naive Bayes Classification and a symptom screening algorithm based on symptom set differences for symptom query process, This algorithm increases the interpretability of diagnostic results by simulating the questioning and diagnostic process of doctors, and combines it with the medical dialogue robot architecture to achieve intelligent diagnosis throughout the entire process.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"143 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this paper is to discuss and apply digital watermarking technology to solve the forgery problem in depth synthesis. With the rapid development of deep synthesis technology and its application in various fields, it is particularly important to protect the authenticity and integrity of digital content. Based on the understanding of digital watermarking, this paper explores an experimental design, which uses watermarking embedding and extraction algorithms and forgery detection technology to solve the problem of deep forgery, protect the copyright, integrity and anti-copy of digital products. In order to improve the robustness and reliability of the watermark, a suitable watermark embedding and extraction algorithm is designed by analyzing the characteristics of deep synthesis forged media in the experimental process. Then select the data set containing the original digital media and the deep synthetic forged samples, extract the features of the two, and find out the features that distinguish the differences between the two. Finally, the forgery detection technology is used to evaluate the performance of digital watermarking technology in depth forgery detection. In this paper, digital watermarking technology is used to provide an effective solution to the problem of forgery in depth synthesis, which can be applied to protect intellectual property rights, prevent tampering and forgery, and protect the authenticity and integrity of digital media content.
{"title":"Counterfeiting in Depth Synthesis based on Digital Watermarking","authors":"Yu Liang, Yadong Yu, Yina Wang, Dunjun Li, Zejiong Zhou","doi":"10.54097/fcis.v5i3.13998","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13998","url":null,"abstract":"The purpose of this paper is to discuss and apply digital watermarking technology to solve the forgery problem in depth synthesis. With the rapid development of deep synthesis technology and its application in various fields, it is particularly important to protect the authenticity and integrity of digital content. Based on the understanding of digital watermarking, this paper explores an experimental design, which uses watermarking embedding and extraction algorithms and forgery detection technology to solve the problem of deep forgery, protect the copyright, integrity and anti-copy of digital products. In order to improve the robustness and reliability of the watermark, a suitable watermark embedding and extraction algorithm is designed by analyzing the characteristics of deep synthesis forged media in the experimental process. Then select the data set containing the original digital media and the deep synthetic forged samples, extract the features of the two, and find out the features that distinguish the differences between the two. Finally, the forgery detection technology is used to evaluate the performance of digital watermarking technology in depth forgery detection. In this paper, digital watermarking technology is used to provide an effective solution to the problem of forgery in depth synthesis, which can be applied to protect intellectual property rights, prevent tampering and forgery, and protect the authenticity and integrity of digital media content.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13837
Daoyang Ming, Weicheng Xiong
Code summarization provides the main aim described in natural language of the given function; it can benefit many tasks in software engineering. Due to the special grammar and syntax structure of programming languages and various shortcomings of different deep neural networks, the accuracy of existing code summarization approaches is not good enough. We proposes to use abstract syntax trees for source code summarization .Our solution is inspired by recent advances in neural machine translation, as well as an approach called SBT by Hu et al. We evaluate our approach using the automated metric BLEU and compare it to other relevant models.
代码总结提供了用自然语言描述的给定函数的主要目的；它能使软件工程中的许多任务受益。由于编程语言的特殊语法和语法结构以及不同深度神经网络的各种缺陷，现有代码摘要方法的准确性不够高。我们建议使用抽象语法树进行源代码摘要，我们的解决方案受到了神经机器翻译最新进展以及 Hu 等人提出的 SBT 方法的启发。
{"title":"The Source Code Comment Generation based on Abstract Syntax Tree","authors":"Daoyang Ming, Weicheng Xiong","doi":"10.54097/fcis.v5i3.13837","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13837","url":null,"abstract":"Code summarization provides the main aim described in natural language of the given function; it can benefit many tasks in software engineering. Due to the special grammar and syntax structure of programming languages and various shortcomings of different deep neural networks, the accuracy of existing code summarization approaches is not good enough. We proposes to use abstract syntax trees for source code summarization .Our solution is inspired by recent advances in neural machine translation, as well as an approach called SBT by Hu et al. We evaluate our approach using the automated metric BLEU and compare it to other relevant models.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"8 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13852
Chen Liu, Fanrun Meng, Zhiren Zhu, Liming Zhou
With the development of technology, unmanned aerial vehicles (UAVs) have shed their military uses and gradually expanded to civilian and commercial fields. With the development of drone technology, object detection technology based on deep learning has become an important research topic in the field of drone applications. Apply object detection technology to unmanned aerial vehicles to achieve object detection and recognition of ground scenes from an aerial perspective. However, in aerial images taken by drones, the detection objects are mostly small targets, and the target scale changes greatly due to the influence of aerial perspective; The image background is complex, and the target object is easily occluded. It has brought many challenges to the target detection of unmanned aerial vehicles. Conventional object detection algorithms cannot guarantee detection accuracy when applied to drones, and optimizing the target detection performance of drones has become an important research topic in the field of drone applications. We improve the WIoUv3 loss function on the basis of YOLOv8s to reduce regression localization loss during training and improve the regression accuracy of the model. The experimental results indicate that the improved model mAP@0.5 It increased by 0.6 percentage points to 40.7%.
{"title":"Object Detection of UAV Aerial Image based on YOLOv8","authors":"Chen Liu, Fanrun Meng, Zhiren Zhu, Liming Zhou","doi":"10.54097/fcis.v5i3.13852","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13852","url":null,"abstract":"With the development of technology, unmanned aerial vehicles (UAVs) have shed their military uses and gradually expanded to civilian and commercial fields. With the development of drone technology, object detection technology based on deep learning has become an important research topic in the field of drone applications. Apply object detection technology to unmanned aerial vehicles to achieve object detection and recognition of ground scenes from an aerial perspective. However, in aerial images taken by drones, the detection objects are mostly small targets, and the target scale changes greatly due to the influence of aerial perspective; The image background is complex, and the target object is easily occluded. It has brought many challenges to the target detection of unmanned aerial vehicles. Conventional object detection algorithms cannot guarantee detection accuracy when applied to drones, and optimizing the target detection performance of drones has become an important research topic in the field of drone applications. We improve the WIoUv3 loss function on the basis of YOLOv8s to reduce regression localization loss during training and improve the regression accuracy of the model. The experimental results indicate that the improved model mAP@0.5 It increased by 0.6 percentage points to 40.7%.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"11 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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