Qihang Zhao, Bin Zhou, Ben Wang, Jin Lu, Luxiao Zhu
With the development of satellite remote sensing technology, the quality and quantity of remote sensing images are constantly improved. Remote sensing feature classification is also playing an increasingly important role in urban planning, resource exploration and other fields. In the early stage of remote sensing feature classification, machine learning algorithms such as SVM and K-means are mainly used. Nowadays, with the expansion of deep learning, various kinds of research in the computer vision field emerge in an endless manner. Remote sensing images are also mostly classified by different neural networks. According to the characteristics and advantages of U-NET, channel attention mechanism, ResNet, large convolution kernel and structural reparameterization, this paper proposes a network structure called RA-UNET. This paper uses the remote sensing ground object classification dataset LoveDA to conduct experiments. The results show that the network classification effect of this paper is better, with mIoU reaching 59.4% and mPA reaching 72.6%. And use the network in this paper and the four mainstream neural networks of FCN, SegNet, PSPNet and UNet to conduct comparative experiments. The comparative experimental results show that the classification effect of the network in this paper is better than the above four mainstream neural networks.
{"title":"Research on remote sensing image classification based on RA-UNet","authors":"Qihang Zhao, Bin Zhou, Ben Wang, Jin Lu, Luxiao Zhu","doi":"10.1117/12.2667743","DOIUrl":"https://doi.org/10.1117/12.2667743","url":null,"abstract":"With the development of satellite remote sensing technology, the quality and quantity of remote sensing images are constantly improved. Remote sensing feature classification is also playing an increasingly important role in urban planning, resource exploration and other fields. In the early stage of remote sensing feature classification, machine learning algorithms such as SVM and K-means are mainly used. Nowadays, with the expansion of deep learning, various kinds of research in the computer vision field emerge in an endless manner. Remote sensing images are also mostly classified by different neural networks. According to the characteristics and advantages of U-NET, channel attention mechanism, ResNet, large convolution kernel and structural reparameterization, this paper proposes a network structure called RA-UNET. This paper uses the remote sensing ground object classification dataset LoveDA to conduct experiments. The results show that the network classification effect of this paper is better, with mIoU reaching 59.4% and mPA reaching 72.6%. And use the network in this paper and the four mainstream neural networks of FCN, SegNet, PSPNet and UNet to conduct comparative experiments. The comparative experimental results show that the classification effect of the network in this paper is better than the above four mainstream neural networks.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128396464","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 target tracking and object tracking are defined in this paper and the difference between multi-target tracking and multi-object tracking is also be illustrated. The Bayes filter, Kalman filter, EKF, JPDA and Hungarian Algorithm are introduced with formulars and an example of moving camera to track the pedestrians used by Kalman filter are shown. In this example, the method which is based on Kalman filter that track pedestrians from a moving car which is installed with camera in the field of the multi-object tracking is analyzed with steps. The algorithm initializes boundary boxes to track the pedestrians and predict the pedestrians based on the previous position. Then, update the tracks and delete the useless tracks. The final step is creating the tracks. After displaying the result, the algorithm based on Kalman filter can successfully track the pedestrians with boundary boxes. However, when the camera is moving fast, some of the pedestrians cannot be recognized.
{"title":"Tracking pedestrians from a moving camera based on Kalman filter","authors":"Yingxu Wang","doi":"10.1117/12.2667813","DOIUrl":"https://doi.org/10.1117/12.2667813","url":null,"abstract":"The target tracking and object tracking are defined in this paper and the difference between multi-target tracking and multi-object tracking is also be illustrated. The Bayes filter, Kalman filter, EKF, JPDA and Hungarian Algorithm are introduced with formulars and an example of moving camera to track the pedestrians used by Kalman filter are shown. In this example, the method which is based on Kalman filter that track pedestrians from a moving car which is installed with camera in the field of the multi-object tracking is analyzed with steps. The algorithm initializes boundary boxes to track the pedestrians and predict the pedestrians based on the previous position. Then, update the tracks and delete the useless tracks. The final step is creating the tracks. After displaying the result, the algorithm based on Kalman filter can successfully track the pedestrians with boundary boxes. However, when the camera is moving fast, some of the pedestrians cannot be recognized.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127274906","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}
In recent years, deep learning has been increasingly used to analyze financial data. For deep learning to predict the buy, sell, and hold points of stocks are prone to over-fitting, unreasonable feature extraction, and other issues. This paper builds a CBAM-CNN model based on Convolutional Neural Network (CNN) and Convolutional Block Attention Module (CBAM) to predict the buy, sell and hold points. In order to verify the applicability and superiority of the proposed method, the shares of Dao 30 and SHH 50 from stock listing to August 11, 2021 are selected, and the accuracy of the deep learning algorithm is evaluated using confusion matrix, weighted F1 score, and Kappa coefficient. The analysis results show that this algorithm has a high classification prediction accuracy because it can identify most of the buy and sell instances and therefore has a better effect. In addition, compared with CNN that do not use the CBAM attention mechanism, classification performance is significantly improved. The results from this analysis can help investors determine their better investment strategies.
{"title":"Stock market trend prediction using CBAM and CNN","authors":"Yong Wang, Zhiyu Xu, Yisheng Li","doi":"10.1117/12.2667378","DOIUrl":"https://doi.org/10.1117/12.2667378","url":null,"abstract":"In recent years, deep learning has been increasingly used to analyze financial data. For deep learning to predict the buy, sell, and hold points of stocks are prone to over-fitting, unreasonable feature extraction, and other issues. This paper builds a CBAM-CNN model based on Convolutional Neural Network (CNN) and Convolutional Block Attention Module (CBAM) to predict the buy, sell and hold points. In order to verify the applicability and superiority of the proposed method, the shares of Dao 30 and SHH 50 from stock listing to August 11, 2021 are selected, and the accuracy of the deep learning algorithm is evaluated using confusion matrix, weighted F1 score, and Kappa coefficient. The analysis results show that this algorithm has a high classification prediction accuracy because it can identify most of the buy and sell instances and therefore has a better effect. In addition, compared with CNN that do not use the CBAM attention mechanism, classification performance is significantly improved. The results from this analysis can help investors determine their better investment strategies.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127294774","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 use of blockchain technology in high-elastic power grids often requires the deployment of blockchain nodes in charging piles, substations, new energy vehicles and other equipment, rather than in traditional servers. Limited by equipment performance and deployment conditions, the technical implementation of blockchain also needs to be optimized accordingly. In this paper, a novel hash scheme without multiple iterations which is suitable for high-elastic power grid is proposed. The hash scheme is constructed using large scale bool functions, which meet the requirements of balance, nonlinearity and SAC. The characteristics of the hash algorithm are also suitable for hardware implementation, which can build hardware micro services in the deployment architecture of high-elastic power grids.
{"title":"A novel hash scheme for high elastic grid blockchain","authors":"Ying Yao, Xianke Zhou, Zhifei Pang, Yong Yan, Liangyu Zha","doi":"10.1117/12.2667491","DOIUrl":"https://doi.org/10.1117/12.2667491","url":null,"abstract":"The use of blockchain technology in high-elastic power grids often requires the deployment of blockchain nodes in charging piles, substations, new energy vehicles and other equipment, rather than in traditional servers. Limited by equipment performance and deployment conditions, the technical implementation of blockchain also needs to be optimized accordingly. In this paper, a novel hash scheme without multiple iterations which is suitable for high-elastic power grid is proposed. The hash scheme is constructed using large scale bool functions, which meet the requirements of balance, nonlinearity and SAC. The characteristics of the hash algorithm are also suitable for hardware implementation, which can build hardware micro services in the deployment architecture of high-elastic power grids.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125215011","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}
With the development of Industrial Internet of Things, the types and functions of components are increasing, the application environment is becoming more and more complex. Also, the quality management of components is becoming more and more important. In order to understand the knowledge related to component quality management more conveniently and build an intelligent system for component quality management, this paper proposes a method to construct component quality management knowledge graph based on BERT word embedding model and entity relationship joint extraction method based on annotation strategy. Combining entity extraction and relationship extraction parts into one not only reduces the consumption of computing resources, but also reduces the propagation of wrong entities. In this paper, the sequence to sequence model of Bert-BilSTm-CRF is adopted. Through the BERT word embedding layer, the context information can be better utilized and the accuracy of extraction can be improved. Experimental results show that compared with other classical deep learning term extraction models, this model has a significant improvement in accuracy, recall rate and F1 value.
{"title":"Knowledge graph construction of component quality management","authors":"Haiming Zhang, Xiaoming Fan, Jiaqi Zhang, Chengzhi Jiang, Jiang Li, Hantian Gu, Bo-wen Li, Hao Hu, Chengxi Liu","doi":"10.1117/12.2667430","DOIUrl":"https://doi.org/10.1117/12.2667430","url":null,"abstract":"With the development of Industrial Internet of Things, the types and functions of components are increasing, the application environment is becoming more and more complex. Also, the quality management of components is becoming more and more important. In order to understand the knowledge related to component quality management more conveniently and build an intelligent system for component quality management, this paper proposes a method to construct component quality management knowledge graph based on BERT word embedding model and entity relationship joint extraction method based on annotation strategy. Combining entity extraction and relationship extraction parts into one not only reduces the consumption of computing resources, but also reduces the propagation of wrong entities. In this paper, the sequence to sequence model of Bert-BilSTm-CRF is adopted. Through the BERT word embedding layer, the context information can be better utilized and the accuracy of extraction can be improved. Experimental results show that compared with other classical deep learning term extraction models, this model has a significant improvement in accuracy, recall rate and F1 value.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117347551","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}
Wi-Fi is a popular wireless local area network technology, which has the characteristics of convenient networking and easy expansion. The existing data remote monitoring system mainly uses ZigBee technology to transmit monitoring data, and the response of the monitoring system takes a long time. Therefore, this paper proposes a remote monitoring system based on Wi-Fi technology. Firstly, a framework including intelligent perception layer, data communication layer and data integration layer is designed to realize the real-time data acquisition of the Internet of Things. Then, a data communication mechanism with high transmission rate is established by the Wi-Fi technology to realize the wireless transmission of monitoring data. Finally, the abnormal data judgment module is designed by using BP neural network to further analyze the real-time data of the Internet of Things. The abnormal monitoring results of the real-time data of the Internet of Things are obtained, and the monitoring results are presented through a visual interface. The system test results show that the total response time of the proposed system is 7440ms, which is reduced by 37. 2% and 42. 89% compared with the CAN-based and PLC-based systems. At the same time, the system realizes the intelligent analysis and efficient monitoring of Internet of Things data and promotes the development of data remote monitoring technology.
{"title":"Internet of Things real-time data remote monitoring system based on Wi-Fi technology","authors":"Feng Liu, Peiwei Wang, Peishun Ye","doi":"10.1117/12.2667926","DOIUrl":"https://doi.org/10.1117/12.2667926","url":null,"abstract":"Wi-Fi is a popular wireless local area network technology, which has the characteristics of convenient networking and easy expansion. The existing data remote monitoring system mainly uses ZigBee technology to transmit monitoring data, and the response of the monitoring system takes a long time. Therefore, this paper proposes a remote monitoring system based on Wi-Fi technology. Firstly, a framework including intelligent perception layer, data communication layer and data integration layer is designed to realize the real-time data acquisition of the Internet of Things. Then, a data communication mechanism with high transmission rate is established by the Wi-Fi technology to realize the wireless transmission of monitoring data. Finally, the abnormal data judgment module is designed by using BP neural network to further analyze the real-time data of the Internet of Things. The abnormal monitoring results of the real-time data of the Internet of Things are obtained, and the monitoring results are presented through a visual interface. The system test results show that the total response time of the proposed system is 7440ms, which is reduced by 37. 2% and 42. 89% compared with the CAN-based and PLC-based systems. At the same time, the system realizes the intelligent analysis and efficient monitoring of Internet of Things data and promotes the development of data remote monitoring technology.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127373351","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}
Ruijia Ma, Yanjia Luo, Ke-Fan Xie, Peng Li, Jie Wu
In the treatment of substations, it is very crucial to make a reasonable arrangement of route used for the maintenance of each substation. Moreover, given the urgency degree of different substations, the priority of each substation should be carefully considered for a good arrangement of route used for the maintenance. In this paper, considering the complexity of the routing arrangement, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) were adopted with the designed priority coding methods and priority constraints for a more reasonable arrangement of route. Moreover, with the analysis of the performances of GA and ACO on the priority-based routing arrangement, a fused method was designed to obtain a good routing arrangement in an efficient manner. The experimental results show that, with the designed priority coding method and the priority constraints, a more reason result can be obtained by the fusion-based method.
{"title":"Substation priority maintenance planning based on genetic ant colony algorithm","authors":"Ruijia Ma, Yanjia Luo, Ke-Fan Xie, Peng Li, Jie Wu","doi":"10.1117/12.2667336","DOIUrl":"https://doi.org/10.1117/12.2667336","url":null,"abstract":"In the treatment of substations, it is very crucial to make a reasonable arrangement of route used for the maintenance of each substation. Moreover, given the urgency degree of different substations, the priority of each substation should be carefully considered for a good arrangement of route used for the maintenance. In this paper, considering the complexity of the routing arrangement, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) were adopted with the designed priority coding methods and priority constraints for a more reasonable arrangement of route. Moreover, with the analysis of the performances of GA and ACO on the priority-based routing arrangement, a fused method was designed to obtain a good routing arrangement in an efficient manner. The experimental results show that, with the designed priority coding method and the priority constraints, a more reason result can be obtained by the fusion-based method.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132658181","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}
J. Xu, Lijun Wang, Jing Xu, Huan He, Jiaying Li, J. Liao
Entity extraction is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations, persons...), which is a very important and fundamental problem in natural language processing. On the research of entity extraction, numerous models ignore the learning of grammatical structure. Considering the shortcomings of previous models, this paper first proposes the PALC (POStag-Attention-LSTM-CRF) model, which adds POS (part of speech) features to entity extraction. Specially, PALC fuses POS features with other features through a multi-layer bidirectional LSTM network and attention mechanism to improve the effect of entity extraction. The experimental results show that the accuracy of the PALC model in this paper on the CONLL03 dataset can be 90.65%, on the CONLL03 dataset can be 84.86%, and on OntoNote 5.0 English dataset can be 86.99%.
{"title":"Entity extraction based on the parts of speech attention mechanism","authors":"J. Xu, Lijun Wang, Jing Xu, Huan He, Jiaying Li, J. Liao","doi":"10.1117/12.2667496","DOIUrl":"https://doi.org/10.1117/12.2667496","url":null,"abstract":"Entity extraction is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations, persons...), which is a very important and fundamental problem in natural language processing. On the research of entity extraction, numerous models ignore the learning of grammatical structure. Considering the shortcomings of previous models, this paper first proposes the PALC (POStag-Attention-LSTM-CRF) model, which adds POS (part of speech) features to entity extraction. Specially, PALC fuses POS features with other features through a multi-layer bidirectional LSTM network and attention mechanism to improve the effect of entity extraction. The experimental results show that the accuracy of the PALC model in this paper on the CONLL03 dataset can be 90.65%, on the CONLL03 dataset can be 84.86%, and on OntoNote 5.0 English dataset can be 86.99%.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128152907","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}
Guohan Ma, Q. Zheng, Jingfeng Wang, Tianyi Zhang, Da Chang, Xingli Li
Aiming at the problem of supply and demand that a large number of charging piles are idle, and the charging of some users is not satisfied, an Immune Algorithm (IA) based charging pile location optimization model is proposed. Give full consideration to affect the charging pile location of multivariate data, such as the data, the number of parking queued up for the charging data, etc., has established the mathematical model of charging pile location selection problem. Secondly, from the perspective of the user, based on the user to the shortest charging pile distance, time, at least for the target's location optimization model, in the regional scale adaptive search for charging pile location. Finally, through the simulation experiment, the rationality and effectiveness of the immune algorithm for optimizing the location of charging piles are verified, which provides a reference for the scientific location of charging piles.
{"title":"Research on optimization of charging pile siting based on immune algorithm","authors":"Guohan Ma, Q. Zheng, Jingfeng Wang, Tianyi Zhang, Da Chang, Xingli Li","doi":"10.1117/12.2668802","DOIUrl":"https://doi.org/10.1117/12.2668802","url":null,"abstract":"Aiming at the problem of supply and demand that a large number of charging piles are idle, and the charging of some users is not satisfied, an Immune Algorithm (IA) based charging pile location optimization model is proposed. Give full consideration to affect the charging pile location of multivariate data, such as the data, the number of parking queued up for the charging data, etc., has established the mathematical model of charging pile location selection problem. Secondly, from the perspective of the user, based on the user to the shortest charging pile distance, time, at least for the target's location optimization model, in the regional scale adaptive search for charging pile location. Finally, through the simulation experiment, the rationality and effectiveness of the immune algorithm for optimizing the location of charging piles are verified, which provides a reference for the scientific location of charging piles.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"208 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132014584","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}
Existing vehicle detection methods lack the fine vehicle detection algorithm. In order to improve the accuracy and applicability of anchor-based object detection models, a novel and practical vehicle Fine-grained identification network (EFDet-SPP) based on the EfficientDet is proposed. The improved network adds a Spatial Pyramid Pooling module (SPP) after the feature extraction network for concatenating features to enhance network learning capabilities, and multi-scale extraction of highly semantic features of images. Anchor-based predictions are converted to pixel-based predictions by combining FCOS's head network, eliminating the hyperparameters associated with anchor boxes. And with Mosaic, Copy-Paste data augmentation methods scale small object samples to achieve data sample balance. Experimental results show that the improved network has achieved 94.8% in the actual collected fine vehicle detection dataset, which is greatly improved compared with the EfficientDet network, and does not significantly increase the training parameters and calculation amount of the network.
{"title":"EFDet-SPP: efficient anchor-free network for fine vehicle detection","authors":"Yongsheng Xie, Ming Ye, Zhe Zhang, He Liu","doi":"10.1117/12.2667701","DOIUrl":"https://doi.org/10.1117/12.2667701","url":null,"abstract":"Existing vehicle detection methods lack the fine vehicle detection algorithm. In order to improve the accuracy and applicability of anchor-based object detection models, a novel and practical vehicle Fine-grained identification network (EFDet-SPP) based on the EfficientDet is proposed. The improved network adds a Spatial Pyramid Pooling module (SPP) after the feature extraction network for concatenating features to enhance network learning capabilities, and multi-scale extraction of highly semantic features of images. Anchor-based predictions are converted to pixel-based predictions by combining FCOS's head network, eliminating the hyperparameters associated with anchor boxes. And with Mosaic, Copy-Paste data augmentation methods scale small object samples to achieve data sample balance. Experimental results show that the improved network has achieved 94.8% in the actual collected fine vehicle detection dataset, which is greatly improved compared with the EfficientDet network, and does not significantly increase the training parameters and calculation amount of the network.","PeriodicalId":345723,"journal":{"name":"Fifth International Conference on Computer Information Science and Artificial Intelligence","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122909171","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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