2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)最新文献_第4页

Lightweight defect detection method of punched nickel-plated steel strip based on GhostNet 基于GhostNet的镀镍冲孔钢带轻量化缺陷检测方法

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00017

Jian-qi Li, Yincong Liang, Rui Du, Jingying Wan, Bin-fang Cao, Hui Liu

引用次数: 0

Collaborative Learning-based Dual Network for Few-Shot Image Classification 基于协作学习的双网络少拍图像分类

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00011

Min Xiong, Wenming Cao, Jianqi Zhong

{"title":"Collaborative Learning-based Dual Network for Few-Shot Image Classification","authors":"Min Xiong, Wenming Cao, Jianqi Zhong","doi":"10.1109/PRMVIA58252.2023.00011","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00011","url":null,"abstract":"With the vigorous development of image classification technology in the field of computer vision, Few-shot learning (FSL) has become a research hotspot for solving classification task model training with a small number of samples. FSL aims to achieve efficient identification and processing of new category samples with few annotations. Previous works focus on information extraction based on one single model for FSL, lacking the distinction of the differences between data samples. Therefore, we present a meta-learning-based dual model with knowledge clustering for few-shot image classification, trying to learn the correlation between dual models and capture the information embedded in the data samples. In addition, we introduce the center loss to cluster the same sort of samples and to maximize the similarity among the intraclass and the difference among the inter-class. We adopt multiple tasks based on Meta-learning during the training stage. For each task, the training of dual models divides into two phases, which depend on each other under the guidance of the center loss. At the first phase, the first model is trained with a soft label obtained by the predicted label of the second model. The second phase repeats the information exchange of the first phase. We find that the optimal predictions of the active model are close to the soft and actual labels. Extensive experimental results on three general benchmarks illustrate the effectiveness of our proposed methods on few-shot classification tasks.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116472575","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

Identification of Dangerous Rural Houses Using Oblique Photogrammetry and Photo Recognition Technology 利用倾斜摄影测量和照片识别技术识别农村危房

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00018

Yin Liu, Fangqiang Yu, Jinglin Xu, Peikang Xin

{"title":"Identification of Dangerous Rural Houses Using Oblique Photogrammetry and Photo Recognition Technology","authors":"Yin Liu, Fangqiang Yu, Jinglin Xu, Peikang Xin","doi":"10.1109/PRMVIA58252.2023.00018","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00018","url":null,"abstract":"Indentify dangerous houses in rural areas isn’t very efficient, considering the large workload to visit the rural area, patchy and untimely manual document’s registration management. This study first uses UAV oblique photography technology to quickly obtain high-resolution aerial photographic images of villages and reconstruct three-dimensional reality models. Then, based on the YOLOv5 algorithm, the features of dangerous houses in aerial photography images are automatically detected, and the features of dangerous houses are mapped to the real 3D model to accurately locate the dangerous buildings. Finally, a digital management platform for rural dangerous houses is developed to support rural managers in identifying, measuring and tracking dangerous houses. The application results in a village along the coast of southern Fujian province showed that the accuracy rate of the final dangerous house screening rate of this method was 92%, and the coverage rate was 95%, which could greatly improve the efficiency, accuracy and coverage of dangerous house screening and reduce the workload of manual screening; and improve management efficiency through platform-based and visual methods.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122565982","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 on Trial: A Case Study to Apply Existing Models to Heterogeneous Datasets 迁移学习试验:将现有模型应用于异构数据集的案例研究

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00054

Lei Jin, Chongxiao Qu, Yongjin Zhang, Changjun Fan, Zhongke Zhu, Shuo Liu

引用次数: 0

Binary-like Real Coding Genetic Algorithm 类二进制实编码遗传算法

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00023

Yongkang Lan

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":null,"pages":null},"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 Priori Lane Selection Strategy for Reinforcement Learning of Dynamic Expressway Tolling 高速公路动态收费强化学习的先验车道选择策略

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00031

Xi Zhang, W. Wang, Jing Chen

引用次数: 0

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":null,"pages":null},"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

Image Dense Captioning of Irregular Regions Based on Visual Saliency 基于视觉显著性的不规则区域图像密集字幕

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00008

Xiaosheng Wen, Ping Jian

{"title":"Image Dense Captioning of Irregular Regions Based on Visual Saliency","authors":"Xiaosheng Wen, Ping Jian","doi":"10.1109/PRMVIA58252.2023.00008","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00008","url":null,"abstract":"Traditional Dense Captioning intends to describe local details of image with natural language. It usually uses target detection first and then describes the contents in the detected bounding box, which will make the description content rich. But captioning based on target detection often lacks the attention to the association between objects and the environment, or between the objects. And for now, there is no dense captioning method has the ability to deal with irregular areas. To solve these problems, we propose a visual-saliency based region division method. It focuses more on areas than just on objects. Based on the division, the local description of the irregular region is carried out. For each area, we combine the image with the target area to generate features, which are put into the caption model. We used the Visual Genome dataset for training and testing. Through experiments, our model is comparable to the baseline under the traditional bounding box. And the description of irregular region generated by our method is equally good. Our model performs well in image retrieval experiments and has less information redundancy. In the application, we support to manually select the region of interest on the image for description, for assist in expanding the dataset.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128857147","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

Object Detection Algorithm for Railway Scenes Based on Infrared and RGB Image Fusion 基于红外和RGB图像融合的铁路场景目标检测算法

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00015

Xin Xu, Haixia Pan, Hongqiang Wang, Yefan Cao

引用次数: 0