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2019 International Conference on intelligent Computing, Automation and Systems, ICICAS 2019

作者： Chen, guangxi Zhan, Baoying Tang, Min Xie, Chen Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (纸本)9781728161068

The problem of pedestrian detection accuracy is low due to partial occlusion of pedestrians in practical application scenarios, a pedestrian occlusion detection method based on improved YOLOv3 network structure is proposed. By improving the activation function and clustering method to strengthen the feature output layer of the shallow layer of the network, which effectively make the accuracy of detection better. The experimental results show that the mAP of proposed method is improved by 2.63 percent in the INRIA data set, and the improved network has good robustness and generalization ability, and guarantees the real-time of detection. © 2019 IEEE.

关键词： Chemical activation

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NPIPVis:A visualization system involving NBA visual analysis and integrated learning model prediction

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Virtual Reality & intelligent Hardware 2022年第5期4卷 444-458页

作者： Zhuo SHI Mingrui LI Meng WANG Jing SHEN Wei CHEN Xiaonan LUO Department of Art and Design University of Guilin Electronic TechnologyGuilin 541004China Department of Computer and Information Security University of Guilin Electronic TechnologyGuilin 541004China Department of Guangxi Key Laboratory of Image and Graphic Intelligent Processing University of Guilin Electronic TechnologyGuilin 541004China Department of State Key Laboratory of CAD&CG University of ZhejiangHangzhou 310058China

Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conc

关键词： Sports visualization Parallel aggregated ordered hypergraph Calliope IStoryline Integrated learning SHAP model

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Semi-supervised Classification on Data Streams with Recurring Concept Drift Based on Conformal Prediction 30th

Semi-supervised Classification on Data Streams with Recurr...

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30th International Conference on Neural Information processing, ICONIP 2023

作者： Ma, ShiLun Kang, Wei Xue, Yun Wen, YiMin Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China School of Municipal and Surveying Engineering Hunan City University Yiyang413000 China

ISBN: (纸本)9789819981830

In this article, we consider the problem of semi-supervised data stream classification. The main difficulties of data stream semi-supervised classification include how to jointly utilize labeled and unlabeled samples to adress concept drift detection and how to use unlabeled to update trained classifier. Existing algorithms like the CPSSDS method constantly retrain a new classifier when concept drift is detected, it is very consuming and wasteful. In this paper, the algorithm of data stream semi-supervised classification with recurring concept drift named as CPSSDS-R is proposed. First, the labeled samples in the first data block are used to initialize a classifier, which is added into a pool and actived for classification. While a new data block arrives, concept drift is detected by computing conformal prediction results. If no concept drift is detected, the pseudo-labeled samples in the previous data block are added with the labeled samples in the current data block to incrementally train the active classifier. If a new concept is detected, a new classifier is trained on the labeled samples of the current data block and added into the pool and actived for classification, else if a recurring concept is detected, the pseudo-labeled samples and labeled samples in the current data block are used to incrementally update the classifier corresponding to the recurring concept in the pool and actived for classification. The proposed algorithm is tested on multiple synthetic and real datasets, and its cumulative accuracy and block accuracy at different labeling ratios demonstrate the effectiveness of the proposed algorithm. The code for the proposed algorithm is available on https://***/ymw12345/cpssds-r. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification (of information)

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HDMacBERT-FGM-CRF Model-Based Chinese Event Element Extraction 14th

HDMacBERT-FGM-CRF Model-Based Chinese Event Element Extracti...

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14th International Conference on Genetic and Evolutionary Computing, ICGEC 2021

作者： Chen, Peng Qiang, Baohua Wang, Yufeng Wang, Chen Chen, Ruidong Li, Baolian Peng, Bo Zhou, Xiangyu Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China The 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050002 China

ISBN: (纸本)9789811684296

Because of the inaccuracy of event element extraction caused by long text, the errors of pre-training and fine-tuning stages, and the single sample of semantic features in the current Chinese event element extraction model, Hierarchical Decomposition MacBERT and Fast Gradient Method-based Chinese event element extraction algorithm by jointing Conditional Random Field (HDMacBERT-FGM-CRF) is proposed. This model extends absolute location encoding based on hierarchical decomposition so that the HDMacBERT model can directly process more than 512 Token texts. The HDMacBERT model based on the Mask strategy of similar words is used to mitigate errors. The FGM-based approach implements adversarial networks and increases the diversity of semantic feature samples. The P, R and F 1 of the HDMacBERT-FGM-CRF model proposed in this paper are 2.52%, 4.03% and 3.26% higher than those of the classical Chinese event element extraction model BERT-CRF on the DuEE dataset. The experimental results show that the proposed model can learn more comprehensive deep-level semantic information of text and improve Chinese event element extraction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Extraction

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Smart Power Safety Hazard Inspection System Based on YOLOv7 1st

Smart Power Safety Hazard Inspection System Based on YOLOv7

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1st International Conference on Applied Intelligence, ICAI 2023

作者： Liang, Yiheng Li, Xiaoming Deng, Zhenrong School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9789819708260

Power safety is closely related to people’s well-being. Electric power sectors regularly inspect and maintain power lines to guarantee people’s safe and stable use of electricity, and the current mainstream inspection method is to use UAVs for power inspection. Traditional inspection and detection methods have many shortcomings, such as high labor costs, slow inspection speed, low detection efficiency, and single detection targets. In this paper, a set of smart power inspection systems based on YOLOv7 was designed to check the common potential security risks of power lines such as bird nests, insulators, garbage, and hardware. After the detection, the detection report could be generated. The system adopted the YOLOv7 target detection algorithm. The accuracy rate of all detection targets was up to 93.9%, and the mAP @.5% reached 95.9%. Therefore, the algorithm could greatly improve the detection efficiency and greatly facilitate the electric power sectors to check power lines, so it has a high use value. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Inspection

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Real-time arbitrarily-shaped scene text spotting with weakly supervised points

Real-time arbitrarily-shaped scene text spotting with weakly...

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2023 International Conference on Computer graphics, Artificial Intelligence, and Data processing, ICCAID 2023

作者： Zhao, Longyang Zhang, Shuhao Xu, Zhi School of Computer Science and Information Security Guilin University of Electronics Technology No.1 Jinji road Guilin541004 China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9781510677685

Recently, there has been widespread interest in arbitrary-shaped scene text spotting, showing notable progress in both speed and accuracy. However, common issues such as model complexity, slow inference speed, and challenges in deployment persist. For this reason, this paper proposes Weakly Supervised Point-collection Network (WSPNet) for arbitrarily shaped scene text, which adopts deformable convolutional single-shot network architecture to realize multi-branch parallel reading of text, and combines weakly supervised text line point sampling to realize sampling recognition and transcription of arbitrarily shaped text instances. To enhance the robustness of multi-scale fusion, we utilize the Text Feature Fusion (TFF) module to dynamically acquire key features at different scales during the fusion process. This approach aims to reduce discrepancies in scale fusion. Extensive experiments on arbitrarily shaped benchmarks show that WSPNet achieves competitive accuracy and speed. For example, the proposed method achieves an end-to-end text recognition F-measure of 63.4 on the Total-Text dataset at 35.5 FPS, which is competitive with the best results in terms of speed and accuracy balance. © 2024 SPIE.

关键词： Network architecture

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Small Object Detection via a Dense Connection and Feature Enhancement Network 9

Small Object Detection via a Dense Connection and Feature En...

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9th International Conference on Computer and Communications, ICCC 2023

作者： Qiang, Baohua Chen, Lirui Guo, Shuiping Liu, Lingzhi Zhang, Shihao Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China The 7th Research Institute of China Electrics Technology Group Corporation Guangzhou China

ISBN: (纸本)9798350317251

When detecting small objects in complex environment, the features of small objects will become blurred or even lost as the number of network layers increases. To address this problem, we constructed a Dense Connection and Feature Extraction Network, termed DCFEN. First, we designed a dense-connected multi-scale feature enhancement framework, which can effectively extract and fuse multi-scale features. Second, we constructed a dense-connected subnet using dense connections, which enhances the propagation of features and the utilization of shallow feature information, improving the detection performance of small objects. Finally, extensive experimental results demonstrated the detection precision and superiority of our method. © 2023 IEEE.

关键词： Network layers

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Dilation-Supervised Learning: A Novel Strategy for Scale Difference in Retinal Vessel Segmentation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1693-1707页

作者： Wang, Huadeng Zuo, Wenbin Li, Bingbing Pan, Xipeng Liu, Zhenbing Lan, Rushi Luo, Xiaonan Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Ganzhou Municipal Hospital Department of Pathology Ganzhou341000 China

Retinal fundus image segmentation based on deep learning is an important method for auxiliary diagnosis of ophthalmic diseases. Due to the scale difference of the blood vessels and the imbalance between foreground and background pixels in the fundus image, the deep learning network will inevitably ignore thin vessels when downsampling and feature learning. For the scale difference problem, this article aims to tackle its limitation from two perspectives: changing the supervised approach and adapting the feature learning. Correspondingly, a dilation-supervised learning method and an adaptive scale dimensional attention mechanism which are used to construct a two-stage segmentation model is proposed. Moreover, we introduce a quantitative approach to evaluate the scale difference of the blood vessels. With the help of the proposed weighted loss function, the segmentation results are refined, and the class imbalance problem between foreground and background pixels is resolved. Finally, the proposed adaptive threshold selection method is used in the postprocessing of segmentation results. The experiments on DRIVE, STARE, CHASE_DB1, and HRF datasets show that the proposed method achieves better segmentation performance compared with other state-of-the-art methods, and has good generalization ability and robustness. © 2020 IEEE.

关键词： Semantics

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Enhanced YOLOv7-Based Construction Site Environmental Detection Method 4

Enhanced YOLOv7-Based Construction Site Environmental Detect...

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4th International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2023

作者： Liang, Siheng Huang, Wenming Xiao, Yannan Guilin University of Electronic Technology School of Computer and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guilin University of Electronic Technology School of Computer and Information Security Guilin China

ISBN: (纸本)9798350343618

This paper proposes an enhanced approach utilizing the YOLOv7 model for automated construction site environment detection and management. The method leverages high-resolution surveillance cameras and computer vision techniques to achieve this goal. The proposed method starts with data collection of the construction site environment. Then produce accurate bounding boxes. These bounding boxes serve as input to an improved YOLOv7 network model, enabling feature extraction and generating high-scoring feature maps with associated weights. Utilizing these weights, image segmentation is performed to obtain the desired detection heads. Experimental results demonstrate the effectiveness of the method in identifying and annotating various environmental features within the construction site, leading to significant improvements in detection accuracy. Compared to similar techniques, this method offers faster detection speed, higher accuracy, and reduced error rates. The proposed construction site environment detection method provides an efficient and automated solution for on-site management in the construction industry, with practical applications and value. © 2023 IEEE.

关键词： Construction industry

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Deepfake Detection Method Based on Face Edge Bands 9

Deepfake Detection Method Based on Face Edge Bands

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9th International Conference on Digital Home, ICDH 2022

作者： Deng, Zhengjie Zhang, Bao He, Shuqian Wang, Yizhen Hainan Normal University School of Information Science and Technology Haikou China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Hainan Normal University School of Physics and Electronic Engineering Haikou China

ISBN: (数字)9781665454780

ISBN: (纸本)9781665454780

The rapid development of face forgery technology and the generation of realistic fake videos have caused serious harm to individuals, society and even the country, so it is important to detect deepfake videos. There are many detection methods available for forged videos, but the overall performance is yet to be improved and does not cope well with high quality forged images or videos. Observing that existing forgery algorithms leave synthetic forgery traces at the edges of faces when creating videos, this paper proposes a new method for detecting forged videos. It first finds the face edges from the video frames, then extracts the face edge bands as deep learning inputs and trains them based on EfficientNet-B3 to achieve effective detection of deepfake videos. Experiments show that the method in this paper can achieve more than 99.8% AUC values on all four forgery methods of the Face-Forensics++ dataset. © 2022 IEEE.

关键词： image enhancement

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