Pub Date : 2023-11-14DOI: 10.54097/fcis.v5i3.13861
Tao Chen, Zikun Luo, Jinhui Liu
This paper focuses on an in-depth study of supermarket vegetable pricing and replenishment problems, utilizing a variety of methods such as statistics, prediction models, planning models and other methods of analysis. First, the data were preprocessed, and frequency distribution histograms were drawn, revealing the distribution pattern and correlation between each category and each single product of vegetables through descriptive statistical analysis. Secondly, for the relationship between sales and pricing, the sales unit price was averaged through the cost-plus pricing formula, and MATLAB was used to nonlinearly fit the relationship between the total sales volume of the categories and the sales price, and the fitting result was further optimized through neural network, and a nonlinear planning model was established, and a genetic algorithm was used to solve the daily replenishment volume of supermarkets and the pricing strategy in order to achieve the maximization of revenue. Finally, the top-rated individual products were screened out by entropy weighting method, and a linear programming model was established to predict the replenishment quantity and pricing strategy for the coming day, which further provided effective decision support for the sales management of the supermarket.
{"title":"Research on Vegetable Commodity Pricing and Replenishment based on Planning Models and Genetic Algorithm","authors":"Tao Chen, Zikun Luo, Jinhui Liu","doi":"10.54097/fcis.v5i3.13861","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.13861","url":null,"abstract":"This paper focuses on an in-depth study of supermarket vegetable pricing and replenishment problems, utilizing a variety of methods such as statistics, prediction models, planning models and other methods of analysis. First, the data were preprocessed, and frequency distribution histograms were drawn, revealing the distribution pattern and correlation between each category and each single product of vegetables through descriptive statistical analysis. Secondly, for the relationship between sales and pricing, the sales unit price was averaged through the cost-plus pricing formula, and MATLAB was used to nonlinearly fit the relationship between the total sales volume of the categories and the sales price, and the fitting result was further optimized through neural network, and a nonlinear planning model was established, and a genetic algorithm was used to solve the daily replenishment volume of supermarkets and the pricing strategy in order to achieve the maximization of revenue. Finally, the top-rated individual products were screened out by entropy weighting method, and a linear programming model was established to predict the replenishment quantity and pricing strategy for the coming day, which further provided effective decision support for the sales management of the supermarket.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139276533","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14055
Jing Zhong
To solve the problem of network latency of cloud computing, organizations usually use the edge computing, which means shorter physical distance from the client, or the parallel computing method, which means separate the task to multi cloud servers. However, these two major solutions do not effectively solve the problem of network latency caused by multiple clients accessing the same resources. In this paper, a new strategy is proposed based on the operation mode of Internet Group Management Protocol (IGMP) to solve the networks latency and waste of network resources caused by multiple clients’ access. This paper would perform the comparison tasks by using Amazon Web Services (AWS). To show the differences, there would be a simulated test of 1000 clients who are trying to access cloud resources from one cloud server. By comparing the total time of 1000 clients receiving their resources, the original group takes 5309 seconds for the cloud server to process the tasks. The test group takes 5034 seconds for the cloud server to process the tasks, which is about 5.68% improvement. Through the research, the conclusion is that if cloud resources are partition properly, the grouping strategy could effectively alleviate the networks latency problem of multiple clients.
{"title":"Using IGMP Protocol to Improve the Latency of Cloud Computing","authors":"Jing Zhong","doi":"10.54097/fcis.v5i3.14055","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14055","url":null,"abstract":"To solve the problem of network latency of cloud computing, organizations usually use the edge computing, which means shorter physical distance from the client, or the parallel computing method, which means separate the task to multi cloud servers. However, these two major solutions do not effectively solve the problem of network latency caused by multiple clients accessing the same resources. In this paper, a new strategy is proposed based on the operation mode of Internet Group Management Protocol (IGMP) to solve the networks latency and waste of network resources caused by multiple clients’ access. This paper would perform the comparison tasks by using Amazon Web Services (AWS). To show the differences, there would be a simulated test of 1000 clients who are trying to access cloud resources from one cloud server. By comparing the total time of 1000 clients receiving their resources, the original group takes 5309 seconds for the cloud server to process the tasks. The test group takes 5034 seconds for the cloud server to process the tasks, which is about 5.68% improvement. Through the research, the conclusion is that if cloud resources are partition properly, the grouping strategy could effectively alleviate the networks latency problem of multiple clients.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"230 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139288813","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14021
Sheng Jin
In the detection of escape ladders in the context of smart construction sites, due to the relatively small target size of the escape ladder compared to the entire input image frame, significant environmental interference, and high missed detection and false detection rates, an improved YOLOv5s escape ladder real-time detection algorithm is proposed by combining the attention mechanism network. The model uses CSPLocknet53 as the backbone network for feature extraction, introduces the attention module CA, and integrates spatial and channel information, while increasing a small amount of computation, performance has been significantly improved. Optimize the network structure of YOLOv5s algorithm, strengthen shallow feature weights to enhance small target detection effectiveness, add attention mechanisms to increase the weight of small targets and their surrounding features, and use Mosaic methods for data augmentation to improve detection accuracy and recall. After multiple repeated experiments, these experimental results have proven that the optimized YOLOv5s algorithm for real-time detection of escape ladders has an average detection accuracy (accuracy, recall) of (81.8, 82.6). Compared with the traditional YOLOv5s algorithm, the accuracy and recall have been improved by 1.4% and 1.2%, respectively. The optimized YOLOv5s algorithm can effectively improve the detection accuracy of real-time detection of escape ladders, and improve the detection and resolution performance of small escape ladder targets.
{"title":"Real Time Detection Algorithm for Escape Ladders based on YOLOv5s","authors":"Sheng Jin","doi":"10.54097/fcis.v5i3.14021","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14021","url":null,"abstract":"In the detection of escape ladders in the context of smart construction sites, due to the relatively small target size of the escape ladder compared to the entire input image frame, significant environmental interference, and high missed detection and false detection rates, an improved YOLOv5s escape ladder real-time detection algorithm is proposed by combining the attention mechanism network. The model uses CSPLocknet53 as the backbone network for feature extraction, introduces the attention module CA, and integrates spatial and channel information, while increasing a small amount of computation, performance has been significantly improved. Optimize the network structure of YOLOv5s algorithm, strengthen shallow feature weights to enhance small target detection effectiveness, add attention mechanisms to increase the weight of small targets and their surrounding features, and use Mosaic methods for data augmentation to improve detection accuracy and recall. After multiple repeated experiments, these experimental results have proven that the optimized YOLOv5s algorithm for real-time detection of escape ladders has an average detection accuracy (accuracy, recall) of (81.8, 82.6). Compared with the traditional YOLOv5s algorithm, the accuracy and recall have been improved by 1.4% and 1.2%, respectively. The optimized YOLOv5s algorithm can effectively improve the detection accuracy of real-time detection of escape ladders, and improve the detection and resolution performance of small escape ladder targets.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"136 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139288960","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14040
Yuxuan Hu
The convolutional neural network model is an important algorithm for deep learning, and YOLOv3-tiny based on this model has excellent object detection ability. However, the computational power required by the model is still large, and it is difficult to realize the application in the embedded field. This paper proposes a hardware acceleration method for YOLOv3-tiny and implements it on FPGA platform. Firstly, the fixed-point quantitative processing was carried out for the network, and an appropriate fixed-point strategy was designed with the data accuracy as the index. Secondly, the parallel computing design and pipeline optimization principle were carried out, and the FIFO structure was introduced to shorten the running time. Finally, the experiment was carried out on the Xilinx PYNQ-Z2 platform, and the data were compared with the previous related work.
{"title":"FPGA Hardware Acceleration Research and Implementation of Deep Learning Algorithms","authors":"Yuxuan Hu","doi":"10.54097/fcis.v5i3.14040","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14040","url":null,"abstract":"The convolutional neural network model is an important algorithm for deep learning, and YOLOv3-tiny based on this model has excellent object detection ability. However, the computational power required by the model is still large, and it is difficult to realize the application in the embedded field. This paper proposes a hardware acceleration method for YOLOv3-tiny and implements it on FPGA platform. Firstly, the fixed-point quantitative processing was carried out for the network, and an appropriate fixed-point strategy was designed with the data accuracy as the index. Secondly, the parallel computing design and pipeline optimization principle were carried out, and the FIFO structure was introduced to shorten the running time. Finally, the experiment was carried out on the Xilinx PYNQ-Z2 platform, and the data were compared with the previous related work.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139288977","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14054
Yuru Gao
With the rapid growth of image data, how to efficiently and accurately extract useful features from massive image data and perform fast image retrieval has become an important research direction. This study focuses on the design and training of deep learning-based image feature extraction networks to improve the robustness and generalization of image features by optimizing the network structure and loss function. In order to evaluate the performance of the system, this study also designs appropriate evaluation indicators and conducts corresponding experiments. Through experimental verification, the results show that these methods can effectively improve the performance of image feature extraction and image retrieval, and have broad potential in practical applications.
{"title":"Image Retrieval based on Deep Learning","authors":"Yuru Gao","doi":"10.54097/fcis.v5i3.14054","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14054","url":null,"abstract":"With the rapid growth of image data, how to efficiently and accurately extract useful features from massive image data and perform fast image retrieval has become an important research direction. This study focuses on the design and training of deep learning-based image feature extraction networks to improve the robustness and generalization of image features by optimizing the network structure and loss function. In order to evaluate the performance of the system, this study also designs appropriate evaluation indicators and conducts corresponding experiments. Through experimental verification, the results show that these methods can effectively improve the performance of image feature extraction and image retrieval, and have broad potential in practical applications.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"77 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139289133","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14008
Qiangchun Wang
The metaverse is not just an extension of physical life into the digital realm but a profound change in the new generation of the digital realm. The metaverse is the future integration of all elements, including the Internet, artificial intelligence, virtual reality, immersive experience, blockchain technology, Internet of Things, Internet of Body, cloud computing, and virtual twins. Several dimensions, such as object, subject, time, entity, and virtuality, can divide the metaverse into four primary forms: augmented reality, life log, virtual twin, and virtual reality. The metaverse means that more and more of our lives, labor, leisure, time, wealth, happiness, and relationships will be spent in virtual worlds. The problems in the cloud universe include the illegal collection of private data, violation of personal rights, digital human evil, data security, increased digital divide, robot evil, artificial intelligence discrimination, health issues, and the gamification of life. It is the direction to build the metaverse, keep the digital ecological environment pleasant, and promote the overall development of humanity.
{"title":"On Development of Metaverse and Digital Ecology Safety","authors":"Qiangchun Wang","doi":"10.54097/fcis.v5i3.14008","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14008","url":null,"abstract":"The metaverse is not just an extension of physical life into the digital realm but a profound change in the new generation of the digital realm. The metaverse is the future integration of all elements, including the Internet, artificial intelligence, virtual reality, immersive experience, blockchain technology, Internet of Things, Internet of Body, cloud computing, and virtual twins. Several dimensions, such as object, subject, time, entity, and virtuality, can divide the metaverse into four primary forms: augmented reality, life log, virtual twin, and virtual reality. The metaverse means that more and more of our lives, labor, leisure, time, wealth, happiness, and relationships will be spent in virtual worlds. The problems in the cloud universe include the illegal collection of private data, violation of personal rights, digital human evil, data security, increased digital divide, robot evil, artificial intelligence discrimination, health issues, and the gamification of life. It is the direction to build the metaverse, keep the digital ecological environment pleasant, and promote the overall development of humanity.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139288697","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14015
Fufu Zhang, Tianxing Chen
In the design process of linear conveyors, in order to ensure the accuracy of the specifications and dimensions of each product, a large amount of drawing design work is required. In order to improve drawing efficiency and reduce human errors, this paper designs a parametric auxiliary design system for linear conveyors. By organizing and analyzing the non-standard size change rules of aviation logistics transportation equipment, and based on the .net4.8 framework, using C# language for secondary development of CAD software, in response to the call for national industrial software localization, this study selected the domestic CAD software Haochen CAD as the platform. Through system testing, this system can quickly generate non-standard part drawings through parameter input, improving drawing efficiency while reducing drawing error rates.
{"title":"Research on the Aided Design System of Linear Conveyor Equipment based on Haochen CAD","authors":"Fufu Zhang, Tianxing Chen","doi":"10.54097/fcis.v5i3.14015","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14015","url":null,"abstract":"In the design process of linear conveyors, in order to ensure the accuracy of the specifications and dimensions of each product, a large amount of drawing design work is required. In order to improve drawing efficiency and reduce human errors, this paper designs a parametric auxiliary design system for linear conveyors. By organizing and analyzing the non-standard size change rules of aviation logistics transportation equipment, and based on the .net4.8 framework, using C# language for secondary development of CAD software, in response to the call for national industrial software localization, this study selected the domestic CAD software Haochen CAD as the platform. Through system testing, this system can quickly generate non-standard part drawings through parameter input, improving drawing efficiency while reducing drawing error rates.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139289205","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14037
Mengying Cai
Nowadays, pond culture is the main form of freshwater culture in China. However, with the aggravation of water pollution and eutrophication of freshwater ecosystem, it is easy to cause pond water bloom. Water bloom will not only reduce the biodiversity of ponds, but also consume dissolved oxygen and produce toxic and harmful substances, which is of great harm to pond aquaculture. By studying the relationship between the main physical and chemical factors in freshwater aquaculture ponds, this paper studies the causes of water bloom, so as to improve the yield of pond culture and enhance everyone's awareness of environmental protection.
{"title":"Study on Water Purification of Freshwater Aquaculture Pond based on Correlation Analysis","authors":"Mengying Cai","doi":"10.54097/fcis.v5i3.14037","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14037","url":null,"abstract":"Nowadays, pond culture is the main form of freshwater culture in China. However, with the aggravation of water pollution and eutrophication of freshwater ecosystem, it is easy to cause pond water bloom. Water bloom will not only reduce the biodiversity of ponds, but also consume dissolved oxygen and produce toxic and harmful substances, which is of great harm to pond aquaculture. By studying the relationship between the main physical and chemical factors in freshwater aquaculture ponds, this paper studies the causes of water bloom, so as to improve the yield of pond culture and enhance everyone's awareness of environmental protection.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139288726","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14059
Songyin Tan, Hongping Pu
This paper presents a solution for ADS-B signal reception with RTL-SDR based on embedded Linux. This system utilizes an inexpensive RTL-SDR radio receiver for real-time reading of aeronautical messages, and it is experimentally demonstrated that this combination provides a significantly cost-effective way to track aircraft. By using an embedded Linux system, this paper demonstrates the high degree of portability and automation that can be achieved with RTL-SDR and its built-in ADS-B signal reception capability. The solution can be used in a wide range of applications and has high practical value.
本文介绍了一种基于嵌入式 Linux 的 RTL-SDR ADS-B 信号接收解决方案。该系统利用廉价的 RTL-SDR 无线电接收器实时读取航空信息,并通过实验证明这种组合提供了一种极具成本效益的跟踪飞机的方法。通过使用嵌入式 Linux 系统,本文展示了 RTL-SDR 及其内置的 ADS-B 信号接收功能可实现的高度便携性和自动化。该解决方案应用广泛,具有很高的实用价值。
{"title":"Receiving ADS-B Signals on Embedded Linux using RTL-SDR: A Practical Guide","authors":"Songyin Tan, Hongping Pu","doi":"10.54097/fcis.v5i3.14059","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14059","url":null,"abstract":"This paper presents a solution for ADS-B signal reception with RTL-SDR based on embedded Linux. This system utilizes an inexpensive RTL-SDR radio receiver for real-time reading of aeronautical messages, and it is experimentally demonstrated that this combination provides a significantly cost-effective way to track aircraft. By using an embedded Linux system, this paper demonstrates the high degree of portability and automation that can be achieved with RTL-SDR and its built-in ADS-B signal reception capability. The solution can be used in a wide range of applications and has high practical value.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"266 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139288938","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 : 2023-11-05DOI: 10.54097/fcis.v5i3.14020
Chunlin He, Jiaye Wu, Yujie Yang
In order to address the issues of missed detection, false detection, and low accuracy of current road cracks, we propose a road crack recognition model based on improved YOLOv5. Firstly, add a CBAM attention module to the backbone network to enhance feature extraction capabilities; Then, a weighted bidirectional feature pyramid (BiFPN) is incorporated into the model for multi-scale feature fusion, replacing the traditional feature pyramid (FPN)+pixel aggregation network (PAN) structure to enhance feature fusion. The experimental results indicate that the improved model outperforms the traditional YOLOV5 model in terms of mAP@0.5 By 17.3%, the improved YOLOv5 algorithm performs well in detecting road cracks and can quickly and accurately identify and locate cracks on the road.
{"title":"Research on Expressway Pavement Crack Detection based on Improved YOLOv5s","authors":"Chunlin He, Jiaye Wu, Yujie Yang","doi":"10.54097/fcis.v5i3.14020","DOIUrl":"https://doi.org/10.54097/fcis.v5i3.14020","url":null,"abstract":"In order to address the issues of missed detection, false detection, and low accuracy of current road cracks, we propose a road crack recognition model based on improved YOLOv5. Firstly, add a CBAM attention module to the backbone network to enhance feature extraction capabilities; Then, a weighted bidirectional feature pyramid (BiFPN) is incorporated into the model for multi-scale feature fusion, replacing the traditional feature pyramid (FPN)+pixel aggregation network (PAN) structure to enhance feature fusion. The experimental results indicate that the improved model outperforms the traditional YOLOV5 model in terms of mAP@0.5 By 17.3%, the improved YOLOv5 algorithm performs well in detecting road cracks and can quickly and accurately identify and locate cracks on the road.","PeriodicalId":346823,"journal":{"name":"Frontiers in Computing and Intelligent Systems","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139289016","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}