2022 2nd International Conference on Networking Systems of AI (INSAI)最新文献_第2页

A Novel Fire Detection Method Based on Deep Neural Network and Image Processing 基于深度神经网络和图像处理的火灾探测新方法

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00022

Yuning Wang

引用次数: 0

Energy Efficiency Optimization for DAS Based on Neural Network 基于神经网络的DAS能效优化

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00054

Yifan Liu, Hai‐Ping Wang, Ni Ma

引用次数: 0

Night Pedestrian Detection Method Based on Image Fusion 基于图像融合的夜间行人检测方法

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00025

Yiming Jiang, S. Chai, Bai-wen Zhang

{"title":"Night Pedestrian Detection Method Based on Image Fusion","authors":"Yiming Jiang, S. Chai, Bai-wen Zhang","doi":"10.1109/INSAI56792.2022.00025","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00025","url":null,"abstract":"Improving pedestrian detection accuracy under conditions of insufficient light at night is a priority for automatic driving. The clarity of conventional visible images at night cannot be as good as that during the daytime. In contrast, the images captured by infrared cameras are almost unaffected by ambient light changes, but there are also defects of not apparent texture features. Therefore, the pedestrian characteristics can be enhanced by combining the thermal and the visible images, which are not affected by the ambient heat. There have been many types of research on pixel-level image fusion in medical diagnosis, military enhancement and navigation, and it has an excellent application value. In this paper, pixel-level fusion method is applied to the fusion of thermal and visible images, and tests on the pedestrian Dataset CVC-14 acquired in a real-world environment. The YOLOV5 method, which has significant advantages in recognition speed and application flexibility, was used for detection. The experimental results showed that the average recognition accuracy and iteration speed was superior to that of the source image. The performance of maximum fusion, principal component analysis (PCA) fusion based on thermal imaging, and weighted average fusion was outstanding. The method proposed in this paper is not complicated and widely applied, which can be improved by other researchers based on this direction.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126236969","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}

引用次数: 0

Sponsors: INSAI 2022

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/insai56792.2022.00009

引用次数: 0

Research on Mask Wearing Detection of Natural Population Based on Improved YOLOv4 基于改进YOLOv4的自然人群口罩佩戴检测研究

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00012

Qian Zhang, Bingdian Yang, Zhichao Liu

{"title":"Research on Mask Wearing Detection of Natural Population Based on Improved YOLOv4","authors":"Qian Zhang, Bingdian Yang, Zhichao Liu","doi":"10.1109/INSAI56792.2022.00012","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00012","url":null,"abstract":"Recently, the domestic COVID-19 epidemic situation has been serious. At present, the most effective epidemic prevention and control measure is still to wear masks. Therefore, setting up automatic detection devices for wearing masks in public places can better help relevant departments to carry out epidemic prevention and control work. Aiming at the problems of the existing mask detection algorithms, such as low accuracy, poor robustness, and inability to meet the real-time requirements of the proposed method, this paper proposes a new mask wearing detection method based on improved YOLOv4. Specifically, first of all, we add the Coordinate Attention Module to the backbone to coordinate feature fusion and representation. Secondly, we conduct a huge number of network structural improvements to enhance the model's performance and robustness. Thirdly, we deploy the K-means clustering algorithm to make the nine anchor boxes more suitable for our NPMD dataset. The extensive experimental results show that the improved YOLOv4 performs better, exceeding the baseline by 4.06% AP with a comparable speed of 64.37 FPS. It can complete a comprehensive and accurate mask wearing detection task in natural scenes.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"167 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124675892","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}

引用次数: 0

Design of License Plate Recognition System Based on OpenCV 基于OpenCV的车牌识别系统设计

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00013

Chenxu Duan, Shiqiang Luo

{"title":"Design of License Plate Recognition System Based on OpenCV","authors":"Chenxu Duan, Shiqiang Luo","doi":"10.1109/INSAI56792.2022.00013","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00013","url":null,"abstract":"With the continuous improvement of people's living standards, the number and types of motor vehicles are increasing, and it is more and more difficult to manage these vehicles. The most important part of vehicle management information is the license plate of each motor vehicle. They are the identity cards of motor vehicles, so a license plate recognition system that can accurately identify is indispensable. Manual recognition has been difficult to cope with the growing demand for license plate recognition. The traditional license plate recognition system has been overwhelmed. They can not cope with a variety of cars and their license plates, especially the emergence of new energy license plates with green as the background color, resulting in a significant decrease in the accuracy of the traditional license plate recognition system. Therefore, it is necessary to introduce more accurate and efficient technologies in the license plate recognition system, such as widely used machine learning. In this paper, Python as the building language, based on OpenCV library to build a license plate recognition system. The system mainly uses the display function in CV2 and the Gaussian filter grayscale processing function to complete the image display and denoising grayscale processing. Locating the license plate is based on the results of the previous step to extract the corresponding mathematical and color features to complete, and then segment the license plate area of continuous strings, the use of neural networks based on kears framework to identify a single character. Through the test set test of the license plate recognition system, the accuracy of the system to identify the license plate is 93.33 %.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132602202","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}

引用次数: 0

Optimization Strategy and Case Analysis of 5G NSA Wireless Network 5G NSA无线网络优化策略及案例分析

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00016

Mingyan Li

{"title":"Optimization Strategy and Case Analysis of 5G NSA Wireless Network","authors":"Mingyan Li","doi":"10.1109/INSAI56792.2022.00016","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00016","url":null,"abstract":"In June 2019, the Ministry of Industry and Information Technology issued 5G commercial licenses to China Telecom, China Mobile, China Unicom and China Radio and Television, which marked the official entry of China into the first year of 5G commercial use and the penetration of 5G into every corner of social life, redefining traditional industries, improving efficiency and reducing costs, which will better change society. In this paper, first of all, the principle of 5G NSA wireless network optimization, the causes of coverage optimization and optimization methods were discussed in detail based on the architecture of 5G NSA wireless network system. Secondly, in view of the common coverage problems in a city, the main problems existing in the network area were judged through the statistics and analysis of the main indicators of the network, and the network optimization was effectively implemented by means of adjusting the downward inclination angle and the azimuth angle of the antenna, and adjusting the power of the reference signal, thereby improving the utilization rate of network resources and the performance of the network, and effectively improving the performance of the network and the user experience. Based on the actual work, the different problems in the optimization of 5G NSA wireless network were analyzed, and the practical solutions were given, which are practical.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121291572","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}

引用次数: 0

Camera-LiDAR Fusion Based Three-Stages Data Association Framework for 3D Multi-Object Tracking 基于相机-激光雷达融合的三维多目标跟踪三阶段数据关联框架

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00037

Zeguo Fu, Huiliang Shang, Liang Song, Zengwen Li, Changxue Chen

{"title":"Camera-LiDAR Fusion Based Three-Stages Data Association Framework for 3D Multi-Object Tracking","authors":"Zeguo Fu, Huiliang Shang, Liang Song, Zengwen Li, Changxue Chen","doi":"10.1109/INSAI56792.2022.00037","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00037","url":null,"abstract":"3D multi-object tracking (MOT) ensures safe and efficient motion planning and vehicle navigation and plays an important role in perception systems in autonomous driving. Currently MOT is divided into tracking by detection and end-to-end, but most of them are tracking by detection using only single depth sensor such as LiDAR to detect and track objects. However, LiDAR has the limitation of not being able to obtain information about the appearance of the object due to the lack of pixel information, which can lead to obtaining inaccurate detection results thus leading to erratic tracking results. Therefore, in this paper, we propose a novel 3D MOT framework that combines the unique detection advantages of cameras and LiDAR. To avoid the IDs generated by the early death of the detection of the same object that produced low scores in successive frames, we design a 3D MOT framework with three-stages data association. And we also design a data association metric based on 3D IoU and Mahalanobis distance. The camera-LiDAR fusion-based 3D MOT framework we propose proves its superiority and flexibility by quantitative experiments and ablation experiments.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121346958","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}

引用次数: 0

Classification of Emotional Stress and Physical Stress Using Electro-Optical Imaging Technology 利用光电成像技术对情绪应激和生理应激进行分类

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00027

Kan Hong

引用次数: 0

Steel Strip Defect Classification Model Based on Convolutional Neural Network Composites 基于卷积神经网络复合材料的钢带缺陷分类模型

2022 2nd International Conference on Networking Systems of AI (INSAI) Pub Date : 2022-10-01 DOI: 10.1109/INSAI56792.2022.00024

Fei Ye, Linjie Bian

{"title":"Steel Strip Defect Classification Model Based on Convolutional Neural Network Composites","authors":"Fei Ye, Linjie Bian","doi":"10.1109/INSAI56792.2022.00024","DOIUrl":"https://doi.org/10.1109/INSAI56792.2022.00024","url":null,"abstract":"In actual industrial scenes, the complex and diverse textures of surface flaw of steel strips lead to poor performance and generalization of the classification and recognition tasks of surface flaw of steel strips. In this paper, an end-to-end classification model of surface flaw of steel strips based on convolution neural network-EDESPNet is proposed. Improve the accuracy of steel belt defect classification. The model inputs defect samples into different branch networks for simultaneous feature extraction, and adds a weight distribution network to enhance category-related feature information, promotes model efficiency and feature spread, and rise the model's portrayal ability. The results indicate that the EDESPNet classification model is surpass VGG19, DenseNet, ResNet50, Xception and other models. The classification precision in the NEU-CLS data set is 94.17%, and the classification accuracy rate in the BS4-CLS data set is 72.52%. The EDESPNet classification model proposed in the article has higher experimental evaluation standards on the BS4-CLS data set than other classification models, and it has the highest correct rate in the NEU-CLS public datasets. The results indicate that the EDESPNet classification model has better recognition task effect and good robustness.","PeriodicalId":318264,"journal":{"name":"2022 2nd International Conference on Networking Systems of AI (INSAI)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116002157","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}

引用次数: 0