2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)最新文献_第5页

DA-YOLOv5: Improved YOLOv5 based on Dual Attention for Object Detection on Coal Chemical Industry DA-YOLOv5:基于双注意的改进YOLOv5煤化工目标检测

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00016

Yan Wang, Haijiang Zhu, Yutong Liu

{"title":"DA-YOLOv5: Improved YOLOv5 based on Dual Attention for Object Detection on Coal Chemical Industry","authors":"Yan Wang, Haijiang Zhu, Yutong Liu","doi":"10.1109/PRMVIA58252.2023.00016","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00016","url":null,"abstract":"The wearing inspection of personnel’s safety protective clothing has important practical significance in the safety production of coal chemical plants. Manual detection or traditional target detection methods are utilized in coal chemical plants for personnel’s safety detection at the moment. However, the clothing detection accuracy is seriously reduced due to the installation position of cameras and the change of light intensity in coal chemical plants. An dual attention based on YOLOv5 is proposed on coal chemical for object detection. Two attention modules, including Efficient Channel Attention (ECA) and Pyramid Split Attention (PSA) module, are integrated into the Spatial Pyramid Pooling (SPP) module and Bottleneck module of this YOLOv5 network. Thus, more global context information is obtained to make up for the lack of global convolution, and the ability to extract features and learn multi-scale information is enhanced. Safety helmet wearing detect data set (SHWD) and self-made data set in our work are utilized to display the improved method’s effectiveness. Compared with the original YOLOv5 algorithm, the improved method achieved an average accuracy increase of 2.7% at different thresholds. Numerous comparative experiments further verify the feasibility of the improved method.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126902769","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

Garbage Classification and Detection Based on Improved YOLOv7 Network 基于改进YOLOv7网络的垃圾分类与检测

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00024

Gengchen Yu, Birui Shao

{"title":"Garbage Classification and Detection Based on Improved YOLOv7 Network","authors":"Gengchen Yu, Birui Shao","doi":"10.1109/prmvia58252.2023.00024","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00024","url":null,"abstract":"With the improvement of people’s living standards, garbage classification is gradually forced. However, due to people’s awareness and knowledge, the classification accuracy and disposal of garbage are difficult to keep pace with guideline changes. With the consideration of the problems of low efficiency, heavy task and poor environment of garbage manual classification, an improved YOLOv7 target detection method is proposed to realize the effective classification of garbage. In this study, the recursive gated convolutional gnconv was used to establish the HorNet network architecture, and the model was trained by making specific data sets. The C3HB module is added to the YOLO model, and the pooling layer is optimized to replace SPPFCSPC to improve the detection accuracy of the target. The experimental results show that the garbage detection and classification method proposed in this study has excellent accuracy. Experiments show that the map value, accuracy and recall rate of the proposed model on garbage datasets are 99.25%, 99.33% and 98.03%, respectively, which are 1.50%, 3.99% and 1.41% higher than those of YOLOv7. The overall results are better than the original model.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115657079","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

A Tag-aware Recommendation Algorithm Based on Deep Learning and Multi-objective Optimization 基于深度学习和多目标优化的标签感知推荐算法

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00013

Yi Zuo, Yun Zhou, Shengzong Liu, Yupeng Liu

{"title":"A Tag-aware Recommendation Algorithm Based on Deep Learning and Multi-objective Optimization","authors":"Yi Zuo, Yun Zhou, Shengzong Liu, Yupeng Liu","doi":"10.1109/prmvia58252.2023.00013","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00013","url":null,"abstract":"Social tagging information to describe characteristics. Recent systems introduce tagging user preferences and item work shows that the recommendation accuracy can be remarkably promoted when tag information is handled properly. However, other performance indicators of recommendations, such as diversity and novelty, are also of great importance in practice. Thus, we propose a two-stage tag-aware multi-objective framework for providing accurate and diversity recommendations. Specifically, we formulate a tag-based recommendation algorithm via deep learning to generate accurate items and abstract effective tag-based potential features for users and items. According to these features, two conflicting objectives are designed to estimate the recommendation accuracy and diversity, respectively. By optimizing these two objectives simultaneously, the designed multi-objective recommendation model can pro-vide a set of recommendation lists for each user. Comparative experiments verify that the proposed model is promising to generate improved recommendations in terms of accuracy and diversity.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130341459","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

Digital Protection and Virtual Display Technology of Ceramic Art 陶瓷艺术的数字保护与虚拟展示技术

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00025

Linghao Cai

{"title":"Digital Protection and Virtual Display Technology of Ceramic Art","authors":"Linghao Cai","doi":"10.1109/prmvia58252.2023.00025","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00025","url":null,"abstract":"Ceramic art has been passed down to the present day. It reflects the wisdom of ancient craftsmen and artists and is a treasure of Chinese culture. People with different cultural backgrounds and different artistic cultivation, their works are rooted in tradition and bold innovation. They are not only traditional and highly skilled skills, but also the essence of national culture. In recent years, with the rapid development of digital media technology, it has provided new development opportunities for the protection and inheritance of intangible cultural heritage. With the intervention of digital technology, the extension of ceramic design is also constantly extending, which requires ceramic designers to continuously expand their knowledge and combine multiple professional subject theories to enrich the connotation of their works. An excellent pottery work is not only a superficial artistic expression, but also a deep cultural heritage and innovative digital media performance. This article will discuss that in the digital age, the digital form of ceramics is obtained through three-dimensional scanning, and the ceramic art is digitally protected; and then the ceramic art is displayed through virtual technology and holographic imaging technology.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122810186","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

Modern Techniques for Rumor Detection from the Perspective of Natural Language Processing 自然语言处理视角下的现代谣言检测技术

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00035

Xinjia Xie, Shun Gai, Han Long

{"title":"Modern Techniques for Rumor Detection from the Perspective of Natural Language Processing","authors":"Xinjia Xie, Shun Gai, Han Long","doi":"10.1109/PRMVIA58252.2023.00035","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00035","url":null,"abstract":"Rumor detection on online social network (OSN) aims to help people retrieve reliable information and prevent public panic when emergencies occur suddenly. However, it is a waste of human efforts to detect rumors from the rapid growth of large-scale datasets. Due to the development of artificial intelligence, many architectures and frameworks are proposed to provide solutions for this issue. The first proposed traditional feature related methods are time-consuming and heavily depend on well-designed features, which calls for novel methods to detect rumors more efficiently. Thus deep neural networks related methods are successively born, and recent research on propagation related methods has captured much attention of both academia and industry. However, there lacks a systematic and global survey in the field of modern rumor detection. In this paper, we introduce rumors and OSN, and then present a comprehensive study of rumor detection methods on OSN, classifying them according to their search approaches and providing a comparison of the selected works. Finally, this survey deliver unique views on key challenges and several future research directions of rumor detection on OSN, such as multi-task learning, multi-modal detection and developing standard datasets and benchmarks. This work is supported by the Department of System Science, College of Liberal Arts and Sciences in National University of Defense Technology.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128990580","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

Knowledge Distillation by Multiple Student Instance Interaction 基于多学生实例交互的知识提炼

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00038

Tian Ni, Haoji Hu

引用次数: 0

A Comparative Study Of CART Algorithm For Forecasting CART预测算法的比较研究

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00028

Juanqin Yan, Quan Zhou, Ya Xiao, Bin Pan

{"title":"A Comparative Study Of CART Algorithm For Forecasting","authors":"Juanqin Yan, Quan Zhou, Ya Xiao, Bin Pan","doi":"10.1109/PRMVIA58252.2023.00028","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00028","url":null,"abstract":"CART algorithm is a tree structure used for classification rules in the form of decision tree from a group of unordered and irregular cases. Compared with other classification methods, it has the advantage that a busy large amount of data can is classified yen fully, and then valuable potential information can be found. The method is simple and intuitive, with fast classification speed and high accuracy, which is suitable for large-scale data processing. Moreover, the algorithm process is easy to understand and can though express the importance of attributes praying attributes. The significant sensitivity and unpredictability of house price make it difficult to construct its forecasting model. In this paper, through an example of house price, the influencing factors of house price are deeply analyzed and the existing research results are systematically sorted out, and the decision tree CART detailed is used to build a molybdenum metal price algorithm model and forecast the actual price. By comparing and analyzing the results by using Not principles, the average absolute error is 4.03%, and the accuracy rate of foreforetrend forecasting trend can reach 94.8%, which shows that the algorithm is not only not intuitive and intuitive, but also reasonable and reliable.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125737698","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

Attention-Based Recursive Autoencoder For Sentence-Level Sentiment Classification 基于注意力的句子级情感分类递归自编码器

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00050

Jiayi Sun, Mingbo Zhao

引用次数: 0

Surface deformation monitoring based on DINSAR technique 基于DINSAR技术的地表变形监测

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00056

Xia Yu, YU Peng, Le Xia, Yuanrong He

引用次数: 0

Spatio-Temporal-based Context Fusion for Video Anomaly Detection 基于时空的上下文融合视频异常检测

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2001-05-02 DOI: 10.1109/PRMVIA58252.2023.00037

Chao Hu, Weibin Qiu, Weijie Wu, Liqiang Zhu

{"title":"Spatio-Temporal-based Context Fusion for Video Anomaly Detection","authors":"Chao Hu, Weibin Qiu, Weijie Wu, Liqiang Zhu","doi":"10.1109/PRMVIA58252.2023.00037","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00037","url":null,"abstract":"Video anomaly detection (VAD) detects target objects such as people and vehicles to discover abnormal events in videos. There are abundant spatio-temporal context information in different objects of videos. Most existing methods pay more attention to temporal context than spatial context in VAD. The spatial context information represents the relationship between the detection target and surrounding targets. Anomaly detection makes a lot of sense. To this end, a video anomaly detection algorithm based on target spatio-temporal context fusion is proposed. Firstly, the target in the video frame is extracted through the target detection network to reduce background interference. Then the optical flow map of two adjacent frames is calculated. Motion features are used multiple targets in the video frame to construct spatial context simultaneously, re-encoding the target appearance and motion features, and finally reconstructing the above features through the spatiotemporal dual-stream network, and using the reconstruction error to represent the abnormal score. The algorithm achieves frame-level AUCs of 98.5% on UCSDped2 and 86.3% on Avenue datasets. On UCSDped2 dataset, the spatio-temporal dual-stream network improves frames by 5.1% and 0.3%, respectively, compared to the temporal and spatial stream networks. After using spatial context encoding, the frame-level AUC is enhanced by 1%, which verifies the method’s effectiveness.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126900951","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: 25