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2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)最新文献

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Infrared Dim Small Target Tracking Based on Inter-frame Consistency Under Complex Background 复杂背景下基于帧间一致性的红外弱小目标跟踪
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00142
Heng Zhou, Zhaoyu Zhang, Zhiheng Chen, Yongqiang Xie, Zhongbo Li
{"title":"Infrared Dim Small Target Tracking Based on Inter-frame Consistency Under Complex Background","authors":"Heng Zhou, Zhaoyu Zhang, Zhiheng Chen, Yongqiang Xie, Zhongbo Li","doi":"10.1109/ICAICE54393.2021.00142","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00142","url":null,"abstract":"Based on the inter-frame correlation and continuity of target features, a method of dim small target tracking in the infrared sequence is proposed. To depress the noise, the spatial hard constraint is used to eliminate the noise that is far away from the target, and the credibility soft constraint evaluates the indistinguishable noise that is close and similar to the target. Aiming at the disappearance of the target caused by the over-bright region, the prior information of the target trajectory of known frames in the past is fully excavated to improve the continuity of target tracking. All the above contributions have been integrated into a unified framework, which is a probabilistic pipeline filtering algorithm with trajectory prediction. The experimental results on five different infrared image sequences prove that the proposed algorithm can effectively improve the robustness and accuracy of tracking in complex environments.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114449170","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: 1
Face Detection under Non-uniform Low Light Based on Improved MTCNN 基于改进MTCNN的非均匀弱光下人脸检测
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00138
Yufei Bao, Rong Dang
{"title":"Face Detection under Non-uniform Low Light Based on Improved MTCNN","authors":"Yufei Bao, Rong Dang","doi":"10.1109/ICAICE54393.2021.00138","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00138","url":null,"abstract":"In order to improve the accuracy of face detection under non-uniform low-light conditions, an image preprocessing method based on illumination compensation and adaptive weight settings are proposed to apply to MTCNN's face detection algorithm. The algorithm uses light compensation theory to preprocess the face image, and then adds an adaptive module to the MTCNN model, which can significantly improve the accuracy and detection rate of MTCNN's face detection under non-uniform low-light conditions, making the accuracy rate reach 93.37 %. Experiments show that under non-uniform low light conditions, this method has better accuracy and robustness than the original MTCNN method, which is beneficial to the later face recognition task.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125405422","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: 1
An External Contour Extraction Method for Pollen Images 花粉图像的外部轮廓提取方法
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00135
Xinyue Zhang, Shi Bao, Chuanying Yang, Shaoying Mad, Jingping Yang
{"title":"An External Contour Extraction Method for Pollen Images","authors":"Xinyue Zhang, Shi Bao, Chuanying Yang, Shaoying Mad, Jingping Yang","doi":"10.1109/icaice54393.2021.00135","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00135","url":null,"abstract":"Pollen allergy has become a common seasonal disease in northern China. This project studies the influence of airborne pollen on allergic rhinitis patients in Inner Mongolia. Through collecting pollen samples, obtaining pollen images, preprocessing and contour extraction, the processed pollen images are more in line with the requirements of deep image processing. In the preprocessing of pollen image, bilateral filter is used to remove noise on the basis of preserving important information of the image, and then canny operator is used for edge detection. According to the edge detection information, the external contour of pollen is extracted. The whole study enables us to understand the pollen shape characteristics more intuitively, which is the most prepared for the subsequent pollen image depth prediction, and provides the necessary learning tools for clinical medical personnel to learn and understand pollen.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128060335","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
An Improved Yolov5 Marine Biological Object Detection Algorithm 一种改进的Yolov5海洋生物目标检测算法
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00014
Haodong Fan, Daqi Zhu, Yuhang Li
{"title":"An Improved Yolov5 Marine Biological Object Detection Algorithm","authors":"Haodong Fan, Daqi Zhu, Yuhang Li","doi":"10.1109/icaice54393.2021.00014","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00014","url":null,"abstract":"YOLO algorithm has high real-time monitoring speed and average accuracy, and also has great advantages for target detection in complex Marine environment. The research of the algorithm must be applied to the equipment eventually, but in most cases, the storage capacity of the equipment is limited, and at the same time, it needs to meet the requirements of high-precision detection. Therefore, this paper proposes an improved Marine biometrics algorithm for YOLOv5 network, which uses GhostNet's idea and introduces GhostBottleneck into YOLOv5. It can be used as a plug and play module to upgrade the existing convolutional neural network. It can reduce the computation of the network and ensure the precision of the network. On this basis, CBAM module is introduced, which combines spatial attention mechanism and channel attention mechanism, and uses multiscale maximum pooling layer to increase the range of receptive field, which can significantly improve the accuracy of image classification and target detection. The experimental results show that compared with the original YOLOv5, the improved model occupies much less storage space and has a greater improvement in the identification accuracy of Marine organisms.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126018198","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: 3
A Study of the Framework of Smart City Management System Construction 智慧城市管理系统建设框架研究
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00112
Yanping Xiao, Puheng Zhang, Tingneng Wang, Ting Li, Zhangliang Song
{"title":"A Study of the Framework of Smart City Management System Construction","authors":"Yanping Xiao, Puheng Zhang, Tingneng Wang, Ting Li, Zhangliang Song","doi":"10.1109/ICAICE54393.2021.00112","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00112","url":null,"abstract":"The overall goal of smart city management system construction is to improve the level and efficiency of city management. Through the application of GIS technology, computer network technology and other advanced means, the basic resource database such as geocoding database and urban two-dimensional and three-dimensional integrated spatial database will be built. Through the construction of intelligent application systems, the intelligence level, networking and spital visualization of urban elements and event management will be achieved. The innovation of the urban management mode should be encouraged to realize the transformation of urban management from passive management to active service and the “scientific, intelligent, strict, fine and long-term” management of the city.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126039954","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
Radar Signal Recognition under Impact Noise Based on Convolutional Neural Network 基于卷积神经网络的冲击噪声下雷达信号识别
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/icaice54393.2021.00154
Zhengyi Qu, Daying Quan, Yun Chen, Xiaofeng Wang
{"title":"Radar Signal Recognition under Impact Noise Based on Convolutional Neural Network","authors":"Zhengyi Qu, Daying Quan, Yun Chen, Xiaofeng Wang","doi":"10.1109/icaice54393.2021.00154","DOIUrl":"https://doi.org/10.1109/icaice54393.2021.00154","url":null,"abstract":"To solve the problem of radar signal recognition under the impact noise along with the conventional Gaussian white noise, we propose a method for radar signal recognition based on Choi-Williams Distribution (CWD) time-frequency transform and convolutional neural network. The α distribution is employed to model the impact noise in radar signals. The proposed method firstly performs CWD time-frequency analysis on the radar signal. Then, two-dimensional time-frequency images obtained by time-frequency transform are fed to a lightweight convolutional neural network for deep feature extraction. Finally, a softmax classifier is used to classify and recognize the radar signals. The simulation results show that the proposed method performs well in the signal classification task, and the lightweight convolutional neural network model provides convenience for realizing FPGA hardware acceleration.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125139707","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
A quantitative analysis method of influencing factors of railway goods arrival time 铁路货物到达时间影响因素的定量分析方法
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00048
Peng Sun, Weijiao Zhang
{"title":"A quantitative analysis method of influencing factors of railway goods arrival time","authors":"Peng Sun, Weijiao Zhang","doi":"10.1109/ICAICE54393.2021.00048","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00048","url":null,"abstract":"Efficient and punctual delivery of railway goods within the delivery period is not only the basic requirement of the state and society for railway transportation enterprises, but also an important rule that railway transportation departments should abide by. Based on this background, this paper studies the main influencing factors of the whole arrival time of railway goods from the aspects of goods themselves, railway technical equipment and railway transportation organization, and quantitatively analyzes the influence degree by using the principal component analysis method. It is found that the most important influencing factors of the whole arrival time of railway goods are waiting for pick-up at the loading station, waiting for delivery at the final technical station and non-operation detention, which provides a solution for the management of railway goods arrival time.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123463971","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
Summary and Experiment on the Exploration of Brain Tumor Image Classification Recognition: Initial Experience of Artificial Intelligence Technology 脑肿瘤图像分类识别探索综述与实验:人工智能技术的初步经验
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00008
Changhao Ding, Wenbo Zheng
{"title":"Summary and Experiment on the Exploration of Brain Tumor Image Classification Recognition: Initial Experience of Artificial Intelligence Technology","authors":"Changhao Ding, Wenbo Zheng","doi":"10.1109/ICAICE54393.2021.00008","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00008","url":null,"abstract":"In this paper, we aim to focus on accurate brain tumor classification. We utilize two popular models, including AlexNet and VGG to realize the recognition of brain tumor dataset. We firstly pre-process and apply data enhancement for the given dataset and then, use those two model for classification. In conclusion, we found that the VGGNet is superior to AlexNet with a large margin and achieves a reasonable performance with 78.2%. Our paper provides a brief attempt for medical image classification.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122144001","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
The Application of BIM Technology in Construction Projects BIM技术在建筑工程中的应用
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00115
Chunhui Zhang
{"title":"The Application of BIM Technology in Construction Projects","authors":"Chunhui Zhang","doi":"10.1109/ICAICE54393.2021.00115","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00115","url":null,"abstract":"With the continuous development of engineering construction informatization, BIM technology can be used as the basic technology of computer-aided design, and its application in the field of engineering construction in China is more and more extensive. In this paper, as through the introduction of the origin, development and application of BIM technology status quo, analysis of BIM technology applied in the construction engineering project management features and advantages, characteristics and actual demand and according to the construction management in our country, put forward the construction project construction management in the application of BIM technology methods, formed a set of engineering construction BIM application the overall implementation plan, It lays the foundation for the realization of bim-based engineering project construction informationization, opens up the whole life cycle of the building, and improves the level of project construction management.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123828485","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
An Improved Faster RCNN Marine Fish Classification Identification Algorithm 一种改进的更快RCNN海鱼分类识别算法
2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE) Pub Date : 2021-11-01 DOI: 10.1109/ICAICE54393.2021.00033
Yuhang Li, Daqi Zhu, Haodong Fan
{"title":"An Improved Faster RCNN Marine Fish Classification Identification Algorithm","authors":"Yuhang Li, Daqi Zhu, Haodong Fan","doi":"10.1109/ICAICE54393.2021.00033","DOIUrl":"https://doi.org/10.1109/ICAICE54393.2021.00033","url":null,"abstract":"For the efficient identification of marine fish species, a fast neural network recognition algorithm based on improved Faster RCNN is proposed.The algorithm first selects residual network (Resnet) with strong feature extraction capability for feature extraction; then generates candidate target regions through 12 different Anchors to further improve the accuracy of detection; finally, the resulting features are transmitted to two subnetworks to achieve classification and positioning respectively.The classification networks are based on the full connectivity structure, while the localization network is mainly composed of convolutional neural networks.This paper verifies the effectiveness of the algorithm on the marine fish (holothurian, echinus, scallop, starfish) image dataset. The results show that the proposed algorithm is more accurate recognition than Faster RCNN while efficiently detecting the target.","PeriodicalId":388444,"journal":{"name":"2021 2nd International Conference on Artificial Intelligence and Computer Engineering (ICAICE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131684046","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: 2
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