2022 4th International Conference on Intelligent Information Processing (IIP)最新文献_第5页

Research on video-based character recognition method for train cargo cars 基于视频的火车车厢字符识别方法研究

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00009

Mingwei Qi, Rentao Zhao, Fei-Fei Zhang, Zhikang Zhao, Ziming Zhu

{"title":"Research on video-based character recognition method for train cargo cars","authors":"Mingwei Qi, Rentao Zhao, Fei-Fei Zhang, Zhikang Zhao, Ziming Zhu","doi":"10.1109/IIP57348.2022.00009","DOIUrl":"https://doi.org/10.1109/IIP57348.2022.00009","url":null,"abstract":"After the train reaches the destination station, the actual arriving train number needs to be confirmed to ensure that the arriving vehicle is accurate. The traditional method of obtaining car numbers is by manually transcribing car numbers, which has the disadvantages of large workload, low efficiency and error-prone. Therefore, this paper designs a video-based train carriage character recognition system, and proposes a method of locating and recognizing train carriage characters based on YOLOv5s and CRNN. The four target detection models, SSD, Faster -RCNN, YOLOV5m, and YOLOv5s, are trained until the models converge completely using 10,000 train carriage datasets manually labeled with Labelimg software. The experimental results show that YOLOv5s outperforms the other models in terms of recognition accuracy and recognition efficiency, with YOLOv5s achieving a detection accuracy of 0.99 for the carriage character region and an average detection speed of 31 frames. The average accuracy of the algorithm is 0.96. Finally, the fusion of YOLOv5s and CRNN models results in a stable average frame rate of more than 20 frames for the detection of complete train carriages. The method in this paper can automatically identify the carriage numbers, which can reduce the labor of workers, avoid the errors caused by manual transcription of records, increase the real-time recording, and is of great significance to the development of railroad transportation system.","PeriodicalId":412907,"journal":{"name":"2022 4th International Conference on Intelligent Information Processing (IIP)","volume":"6 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":"128722689","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

Transfer learning-based Traffic Identification for UAV-Assisted IoT 基于迁移学习的无人机辅助物联网流量识别

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00013

Meng-yuan Zhu, Xin-yu Hong, Zhuo Chen, Jiaxin Zhou, Na Lv

引用次数: 0

Construction of Power Knowledge Graph based on Entity Relation Extraction 基于实体关系抽取的权力知识图谱构建

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00022

Qiong-lan Na, Dan Su, Jiaojiao Zhang, Xin Li, Na Xiao

引用次数: 0

Fault diagnosis of batch process based on improved time convolution network and efficient channel attention 基于改进时间卷积网络和有效通道关注的间歇过程故障诊断

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00033

X. Liang, L. Guo

引用次数: 0

Evaluation of Radar Health Status Based on Fuzzy Comprehensive Evaluation of Cloud Mode 基于云模式模糊综合评价的雷达健康状态评价

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/IIP57348.2022.00027

Yang Li, Qing Hua Liu, Qi Qing Fang, Gen Liu, Ya Min Hu

引用次数: 0

An Aggregation Method based on Cosine Distance Filtering 一种基于余弦距离滤波的聚合方法

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00031

Degang Wang, Yi Sun, Qi Gao, Fan Yang

{"title":"An Aggregation Method based on Cosine Distance Filtering","authors":"Degang Wang, Yi Sun, Qi Gao, Fan Yang","doi":"10.1109/iip57348.2022.00031","DOIUrl":"https://doi.org/10.1109/iip57348.2022.00031","url":null,"abstract":"Federated learning provides privacy protection for source data by exchanging model parameters or gradients. However, it still faces the problem of privacy disclosure. For example, membership inference attack aims to identify whether target data sample is used to train machine learning models in federated learning. Active membership inference attack takes advantage of the feature that attackers can participate in model training in federated learning, actively influence the model update to extract more information about the training set, which greatly increases the risk of model privacy disclosure. Aiming at the problem that the existing secure aggregation methods of federated learning cannot resist the active membership inference attack, DeMiaAgg, an aggregation method based on cosine distance filtering, is proposed. The cosine distance is used to quantify the deviation degree between clients’ gradient vector and global model parameter vector, and the malicious gradient vector is excluded from gradients aggregation to defense against the active membership inference attack. Experiments on the Texas 100 and Location30 datasets show that DeMiaAgg method is superior to the current advanced differential privacy and secure aggregation methods, and can reduce the accuracy of active membership inference attack to the level of passive attacks.","PeriodicalId":412907,"journal":{"name":"2022 4th International Conference on Intelligent Information Processing (IIP)","volume":"12 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":"115606202","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

Deep recurrent neural network for ground-penetrating radar signal denoising 探地雷达信号去噪的深度递归神经网络

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00024

Chongpeng Tian, Mei Hong, Dongying Li, Da Yuan

引用次数: 0

A Deep Fusion Network for Violence Recognition 暴力识别的深度融合网络

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00029

Zhimin Song, Wuwei Zhang, Dongyue Chen

{"title":"A Deep Fusion Network for Violence Recognition","authors":"Zhimin Song, Wuwei Zhang, Dongyue Chen","doi":"10.1109/iip57348.2022.00029","DOIUrl":"https://doi.org/10.1109/iip57348.2022.00029","url":null,"abstract":"With the rise of smart city construction, the importance of violence recognition based on surveillance video is increasingly prominent. For the violence in a surveillance video, it is challenging to define specific violent behavior, and the number of participants engaging in violence is unknown while the involvement of each participant is different. Undoubtedly, these barriers are unfavorable for the consistency of the video frame and video clip labels. In this paper, we propose a new framework of violence recognition: a frame selection strategy based on local differential brightness is designed for the accurate selection of violence frames; meanwhile, a deep fusion network P-VFN is designed, targeting to avoid the mismatch between frames and video labels; finally, various motion image detection algorithms are compared to explore the substitutability of the optical flow method, which aims to better the unsatisfactory real-time performance of current optical flow calculation. Experimental results on three challenging benchmark datasets demonstrate that the proposed approach outperforms many state- of-the-art violence recognition models. Furthermore, to compensate for the lack of the current public dataset in the real surveillance scene, we use real surveillance cameras to capture and produce a largescale real violence dataset, which also attributes to a better performance.","PeriodicalId":412907,"journal":{"name":"2022 4th International Conference on Intelligent Information Processing (IIP)","volume":"89 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":"127148890","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

Longitudinal and transverse trajectory tracking of unmanned vehicle based on dual PID and LQR 基于双PID和LQR的无人驾驶飞行器纵向和横向轨迹跟踪

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00081

Yanan Tu, Y. Wu, Yixuan Li, P. Zhang, Zhijun Guo, Yakun Yin

引用次数: 0

Robust Value Class Data based Block Data Storage 基于鲁棒值类数据的块数据存储

2022 4th International Conference on Intelligent Information Processing (IIP) Pub Date : 2022-10-01 DOI: 10.1109/iip57348.2022.00089

Fei Liang, Xiuli Huang, Peng Gao, Xianzhou Gao

引用次数: 0