检索结果-内蒙古大学图书馆

License plate detection and recognition based on YOLOv3 and ILPRNET

SIGNAL image AND VIDEO processing 2022年第2期16卷 473-480页

作者： Zou, Yongjie Zhang, Yongjun Yan, Jun Jiang, Xiaoxu Huang, Tengjie Fan, Haisheng Cui, Zhongwei Guizhou Univ Coll Comp Sci & Technol Key Lab Intelligent Med Image Anal & Precise Diag Guiyang 550025 Peoples R China Zhuhai Orbita Aerosp Sci & Techol Co Ltd Oribita Tech Pk1 BaiSha Rd Tangjia Dongan 519000 Zhuhai Peoples R China Guizhou Educ Univ Big Data Sci & Intelligent Engn Res Inst Guiyang 550018 Peoples R China

This paper is concerned with the detection and recognition of Chinese license plates in complex backgrounds. Most applications are currently focused on good conditions. In complex natural scenes such as CCPD-DB, CCPD-FN, CCPD-Rotate, CCPD-Tile, CCPD-Weather, and CCPD-Challenge from the Chinese City Parking Dataset (CCPD), inaccurate localization and poor character recognition accuracy issues appear towards existing license plates. Therefore, this paper proposes a two-stage license plate recognition algorithm based on YOLOv3 and Improved License Plate recognition Net (ILPRNET). In the first stage, YOLOv3 is adopted to detect the position of the license plate and then extract the license plate. In the second stage, the ILPRNET license plate recognition network is used to perform localization of license plate characters and the 2D attentional-based license plate recognizer with an CNN encoder is capable of recognizing license plates accurately. The test results indicate that our proposed algorithm performs well in a variety of complex scenarios. Especially in sub-datasets like CCPD-Base, CCPD-DB, CCPD-FN, CCPD-Weather, and CCPD-Challenge, the recognition accuracy achieved 99.2%, 98.1%, 98.5%, 97.8%, and 86.2%, respectively.

关键词： License plate detection Character localization License plate recognition

来源：评论

学校读者我要写书评

暂无评论

A Two-Stage Federated Learning Framework for Class Imbalance in Aerial Scene Classification 6th

A Two-Stage Federated Learning Framework for Class Imbalance...

引用

6th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Lv, Zhengpeng Zhuang, Yihong Yang, Gang Huang, Yue Ding, Xinghao Xiamen Univ Sch Informat Xiamen Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Centralized aerial imagery analysis techniques face two challenges. The first one is the data silos problem, where data is located at different organizations separately. The second challenge is the class imbalance in the overall distribution of aerial scene data, due to the various collecting procedures across organizations. Federated learning (FL) is a method that allows multiple organizations to learn collaboratively from their local data without sharing. This preserves users' privacy and tackles the data silos problem. However, traditional FL methods assume that the datasets are globally balanced, which is not realistic for aerial imagery applications. In this paper, we propose a Two-Stage FL framework (TS-FL), which mitigate the effect of the class imbalanced problem in aerial scene classification under FL. In particular, the framework introduces a feature representation method by combing supervised contrastive learning with knowledge distillation to enhance the model's feature representation ability and minimize the client drift. Experiments on two public aerial datasets demonstrate that the proposed method outperforms other FL methods and possesses good generalization ability.

关键词： Aerial scene analysis Federated learning Class imbalance Two-stage framework remote sensing

来源：评论

学校读者我要写书评

暂无评论

A Study on the Performance of image recognition Technology in Web GIS for Land Use Change Monitoring

引用

Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Wei, Rongnuan Wei, Mulan Zhang, Yongxia Huang, Xiaohua Liao, Cuihuan Department of Natural Resources Engineering Guangxi Natural Resources Vocational and Technical College Guangxi Chongzuo532199 China

In order to monitor land use change more accurately and rationally manage and utilize land resources, a GIS-based land use change monitoring method is proposed. Based on the satellite remote sensing images provided by the data processing module, the filtering and enhancement processing of land satellite remote sensing images has been completed. The classification module processes the remote sensing images and uses the multi-scale land use multi-categorization network to extract the land use classification results. image recognition technology stratifies the scenes and images of the study area, utilizes GIS data and remote sensing images superimposed on each other, uses the internal GIS data as training samples, and transforms the various information existing in GIS to form a knowledge rate and a rule base, which is applied to the RS image classification and recognition process. The fastest rate of land use change in a city was found to be the construction land, with the highest rate of 0.1395%. The degree of utilization of arable land enters into a period of decline, and the unutilized land enters into a period of development. Through the change monitoring module, we can accurately monitor and calculate the dynamics of land use, the rate of change in the degree of utilization, and the results of the transfer matrix. © 2024 Rongnuan Wei, Mulan Wei, Yongxia Zhang, Xiaohua Huang and Cuihuan Liao, published by Sciendo.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

UAV and Deep Learning: Detection of selected riparian species along the Ganga River

UAV and Deep Learning: Detection of selected riparian specie...

引用

ISPRS Technical Commission I Midterm Symposium on Intelligent sensing and remote sensing Application

作者： Tripathi, Ravindra Nath Ramachandran, Aishwarya Agarwal, Karan Tripathi, Vikas Badola, Ruchi Hussain, Syed Ainul Ganga Aqualife Conservation Monitoring Center Wildlife Institute of India Dehradun248001 India Dept. of Computer Science and Engineering Graphic Era deemed to be University Dehradun248002 India Dept. of Remote sensing Birla Institute of Technology Mesra Ranchi835215 India

Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and wellbeing. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 x 3200 resolution. Additionally, our

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Universal adversarial perturbation for remote sensing images 24

Universal adversarial perturbation for remote sensing images

引用

IEEE 24th International Workshop on Multimedia Signal processing (MMSP)

作者： Wang, Qingyu Feng, Guorui Yin, Zhaoxia Luo, Bin Anhui Univ Sch Comp Sci & Technol Hefei Peoples R China Shanghai Univ Sch Commun & Elect Engn Shanghai Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai Peoples R China

ISBN: (数字)9781665471893

ISBN: (纸本)9781665471893

Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditional technology. However, even the state-of-the-art object recognition convolutional neural networks are fooled by the universal adversarial perturbation (UAP). The research on UAP is mostly limited to ordinary images, and RSIs have not been studied. To explore the basic characteristics of UAPs of RSIs, this paper proposes a novel method combining an encoder-decoder network with an attention mechanism to generate the UAP of RSIs. Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model. Finally, the generated regions are used to fine-tune the perturbation making the model misclassified with fewer perturbations. The experimental results show that the UAP can make the classification model misclassify, and the attack success rate of our proposed method on the RSI data set is as high as 97.09%.

关键词： remote sensing image deep learning universal adversarial perturbation encoder-decoder attention mechanism

来源：评论

学校读者我要写书评

暂无评论

A new deep learning approach for classification of hyperspectral images: feature and decision level fusion of spectral and spatial features in multiscale CNN

引用

GEOCARTO INTERNATIONAL 2022年第14期37卷 4208-4233页

作者： Sharifi, Obeid Mokhtarzadeh, Mehdi Beirami, Behnam Asghari KN Toosi Univ Technol Fac Geodesy & Geomat Engn Dept Photogrammetry & Remote Sensing Tehran Iran

Classification is the main field of hyperspectral data processing. To date, many methods are introduced to increase the accuracy of image classification. In recent years, various convolutional neural network models are proposed for hyperspectral image classification. This study puts forward a multiscale structure of convolutional neural networks that use several patches of different sizes to extract complex spatial features. Due to spatial features' effectiveness in improving the classification accuracy of hyperspectral images, the proposed framework integrates spatial features of three methods;morphological profiles, Gabor filter, and local binary pattern with spectral features at both the feature-level and decision-level. The experiments on three hyperspectral images, Indian Pine, Pavia University, and NCALM demonstrate the proposed method's efficiency. The final results show that the proposed method's overall classification accuracy is 6% higher than some other recent techniques.

关键词： Hyperspectral image classification convolutional neural networks morphological profiles Gabor filter local binary pattern

来源：评论

学校读者我要写书评

暂无评论

Object Classification of remote sensing images Based on Optimized Projection Supervised Discrete Hashing 25

Object Classification of Remote Sensing Images Based on Opti...

引用

25th International Conference on pattern recognition (ICPR)

作者： Zhang, QianQian Liu, Yazhou Sun, Quansen Nanjing Univ Sci & Technol Comp Sci & Engn Nanjing Peoples R China

ISBN: (纸本)9781728188089

Recently, with the increasing number of large-scale remote sensing images, the demand for large-scale remote sensing image object classification is growing and attracting the interest of many researchers. Hashing, because of its low memory requirements and high time efficiency, has widely solve the problem of large-scale remote sensing image. Supervised hashing methods mainly leverage the label information of remote sensing image to learn hash function, however, the similarity of the original feature space cannot be well preserved, which can not meet the accurate requirements for object classification of remote sensing image. To solve the mentioned problem, we propose a novel method named Optimized Projection Supervised Discrete Hashing(OPSDH), which jointly learns a discrete binary codes generation and optimized projection constraint model. It uses an effective optimized projection method to further constraint the supervised hash learning and generated hash codes preserve the similarity based on the data label while retaining the similarity of the original feature space. The experimental results show that OPSDH reaches improved performance compared with the existing hash learning methods and demonstrate that the proposed method is more efficient for operational applications.

关键词： remote sensing supervised discrete hashing optimized projection object classification

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Mixed Pixel Network for remote sensing image Super-Resolution

Multi-Scale Mixed Pixel Network for Remote Sensing Image Sup...

引用

pattern recognition and Artificial Intelligence (PRAI), International Conference on

作者： He Yang Xun Ji Xu Wang Shijie Chen School of Marine Electrical Engineering Dalian Maritime University Dalian Liaoning China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

Deep learning-based image super-resolution (SR) technology has gained extensive attention in the remote sensing community, which aims to reconstruct the abundant details of target images. However, the practical application of existing deep learning-based SR methods is often limited by the trade-off between network performance and computational load. This paper presents a novel multi-scale mixed pixel network (MSMP-Net) to explore the precise and cost-effective SR reconstruction of remote sensing images. Specifically, a dual-path feature capture mechanism (DPFCM) architecture is devised that incorporates diverse dilated convolution operations to comprehensively capture hierarchical features across varying receptive fields. In addition, a triple-path high-resolution rebuilder (TPHRR) structure is designed, which uses a set of dense branches, residual branches and pyramid branches to effectively promote the transfer and reuse of features. Extensive experiments reveal that our proposed MSMP-Net outperforms other state-of-the-art SR methods in reconstruction effects.

关键词： Convolution Superresolution Poles and towers Learning (artificial intelligence) Computer architecture Streaming media Feature extraction pattern recognition remote sensing image reconstruction

来源：评论

学校读者我要写书评

暂无评论

Deep Hierarchical Vision Transformer for Hyperspectral and LiDAR Data Classification

引用

IEEE TRANSACTIONS ON image processing 2022年 31卷 3095-3110页

作者： Xue, Zhixiang Tan, Xiong Yu, Xuchu Liu, Bing Yu, Anzhu Zhang, Pengqiang PLA Strateg Support Force Informat Engn Univ Zhengzhou 450001 Peoples R China

In this study, we develop a novel deep hierarchical vision transformer (DHViT) architecture for hyperspectral and light detection and ranging (LiDAR) data joint classification. Current classification methods have limitations in heterogeneous feature representation and information fusion of multi-modality remote sensing data (e.g., hyperspectral and LiDAR data), these shortcomings restrict the collaborative classification accuracy of remote sensing data. The proposed deep hierarchical vision transformer architecture utilizes both the powerful modeling capability of long-range dependencies and strong generalization ability across different domains of the transformer network, which is based exclusively on the self-attention mechanism. Specifically, the spectral sequence transformer is exploited to handle the long-range dependencies along the spectral dimension from hyperspectral images, because all diagnostic spectral bands contribute to the land cover classification. Thereafter, we utilize the spatial hierarchical transformer structure to extract hierarchical spatial features from hyperspectral and LiDAR data, which are also crucial for classification. Furthermore, the cross attention (CA) feature fusion pattern could adaptively and dynamically fuse heterogeneous features from multi-modality data, and this contextual aware fusion mode further improves the collaborative classification performance. Comparative experiments and ablation studies are conducted on three benchmark hyperspectral and LiDAR datasets, and the DHViT model could yield an average overall classification accuracy of 99.58%, 99.55%, and 96.40% on three datasets, respectively, which sufficiently certify the effectiveness and superior performance of the proposed method.

关键词： Feature extraction Transformers Hyperspectral imaging Laser radar Data mining Collaboration Data models Hyperspectral image light detection and ranging joint classification vision transformer convolutional vision transformer cross attention fusion

来源：评论

学校读者我要写书评

暂无评论

Animal Video Retrieval System using image recognition and Relationships Between Concepts of Animal Families and Species 5

Animal Video Retrieval System using Image Recognition and Re...

引用

5th IEEE International Conference on image processing Applications and Systems (IPAS)

作者： Watanabe, Chinatsu Kaneko, Mayu Chandrasiri, Naiwala P. Kogakuin Univ Intelligent Media Proc Lab Tokyo Japan Mizuho Res & Technol Ltd Tokyo Japan

ISBN: (纸本)9781665462198

In recent years, video streaming services have become increasingly popular. In general, the search function in a video sharing service site evaluates the relevance of a search query to the title, tags, description, and so on given by the creator of the video. Then, the search results with the highest relevance are displayed. Therefore, if a title is given to a video that does not match its content, there is a possibility that a video with low relevance will be found. In this research, ( 1) we built a new system that retrieves animal videos that are relevant to its content using image recognition. (2) By describing the relationships between the concepts of animal families and species and incorporating them into the retrieval system, it is possible to retrieve animal videos by their family names. Adding retrieval by animal family name enabled us to find species that have not been learned. In this research, (3) we confirmed the usefulness of our video retrieval system using trained neural networks, GoogLeNet and ResNet50, as animal species classifiers.

关键词： Video Retrieval image recognition Deep Learning Animal Video Relationships between concepts

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：