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4th International Conference on Energy Engineering and Power Systems, EEPS 2024

作者： Jiang, Mengyuan Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350366914

As the demand for power load forecasting continues to grow in modern society, this paper proposes an improved D-KAN model that integrates Kolmogorov-Arnold Networks (KAN) into the DLinear power load time series prediction framework. The model utilizes KAN's learnable activation functions, parameterized by spline functions, to enhance the accuracy of electricity demand forecasting. This study focuses on the electricity load data from Huanggang City, Hubei Province, covering the period from April 2022 to July 2023. Experimental results show that the D-KAN model performs more accurately in short-term predictions compared to the DLinear model. © 2024 IEEE.

关键词： D-KAN Deep Learning electricity load forecasting Kolmogorov-Arnold Networks (KAN) Machine Learning styling Time Series Analysis

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Chinese Text Spotter Exploiting Spatial Semantic Information in Scene Text Images 5

Chinese Text Spotter Exploiting Spatial Semantic Information...

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5th International Conference on robotics and Computer Vision, ICRCV 2023

作者： Wang, Hao Zhou, Huabing College of Computer Science and Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350326369

Chinese scene text spotting plays a significant role in computer vision applications as it involves extracting and interpreting Chinese information from scene images. However, existing methods often lack specialized designs for complex spatial semantic information in Chinese text images, leading to various degrees of false detections in Chinese scene text images. In this paper, we present SSTS, a novel Spatial Semantic Text Spotter for Chinese text image detection and recognition. SSTS utilizes a concise Transformer encoder and decoder architecture to effectively model spatial semantic information and predict the spatial orientation of Chinese text, reducing the likelihood of false detections caused by different combinations of Chinese characters in various orientations. Experimental results on Chinese scene public datasets demonstrate the superiority of our proposed method. In particular, SSTS achieves a new record of 78.5% in the 1-NED metric on the ReCTS dataset. © 2023 IEEE.

关键词： Computer vision

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ID-DA Fusion:Infrared and Visible Image Fusion Based on Intermediate Domain and Dual Attention Mechanism 6

ID-DA Fusion:Infrared and Visible Image Fusion Based on Inte...

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6th International Conference on Frontier Technologies of Information and Computer, ICFTIC 2024

作者： Liu, Shuo Zhou, Huabing Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot College of Computer Science and Engineering Wuhan China

ISBN: (纸本)9798331541750

Infrared and visible image fusion aims to integrate complementary information from source images to produce a fused image with enhanced visual quality and richer semantic content. In this study, we propose a novel fusion framework based on the Intermediate Domain and a Dual Attention Mechanism (ID-DA Fusion). The intermediate domain is introduced to effectively bridge the gap between infrared and visible modalities, enabling better feature alignment and information preservation. Additionally, the dual attention mechanism dynamically adjusts the feature weights by focusing on salient regions and interdependencies between channels, enhancing both global and local feature representation. Experimental results on benchmark datasets demonstrate that the proposed method achieves superior performance in terms of edge preservation, contrast enhancement, and detail retention, outperforming state-of-the-art fusion techniques in quantitative metrics and visual assessments. This approach holds great potential for applications in surveillance, remote sensing, and medical imaging. © 2024 IEEE.

关键词： Image fusion

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Wearing Detection by Multi-Scale Information Fusion Network for Live-Line Working Scenarios 4

Wearing Detection by Multi-Scale Information Fusion Network ...

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Zhou, Junyi Zhang, Yanduo Lu, Tao Fang, Wenhua Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

In live-line working scenarios, properly wearing the insulating protective personal equipment (PPE) is an important means of ensuring the safety of the operator. However, there are many challenges in applying universal object detection for wearing detection in live-line working scenarios. Different cameras are located at different distances from the operator. During the operation, the insulating bucket where the operator stands will keep moving. These can lead to a large range of scale changes of objects in the image, causing a decrease in detection accuracy. In order to solve the above problems, we design a PPE dataset about live-line working and propose a multi-scale information fusion network based on Yolov5. To be specific, in order to suppress the inconsistency between different scales, we employ the adaptive spatial feature fusion module. Then we integrate coordinate attention to make the network pay more attention to the location information, alleviating the problem of small target miss detection. Moreover, we also adopt Varifocal loss to mitigate the category imbalance problem to improve accuracy. The experiments prove that our method outperforms most object detection methods. And the results show that our multiscale information fusion network achieves 90.3% AP50 and 61.4% AP50:95 on our dataset. Compared to Yolov5, our method improves 2.2% AP50 and 2.1% AP50:95. For the shawl category, AP50 improves by 7.4%. © 2022 IEEE.

关键词： Object detection

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Images segmentation method on comparison of feature extraction techniques

Images segmentation method on comparison of feature extracti...

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2nd International Workshop on intelligent Systems and Applications, ISA2010

作者： Wang, Haihui Wang, Yanli Zhao, Tongzhou Wang, Miao Wu, Mingpeng Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology School of Computer Science & Engineering Wuhan Institute of Technology

ISBN: (纸本)9781424458745

An algorithm of segmentation by using feature extraction techniques of Synthetic Aperture Radar (SAR) images in this paper. The segmentation processor are shown to be of interest for analysing SAR image data. The extracted and selected features are then used to train different neural-network based classifiers. Segmentation makes use of wavelet decomposition and unsupervised clustering based on PCA. The learning approach of neural networks is used for combining various features of different areas of an image. The outcomes of the proposed segmentation techniques are compared to the standard Gaussian discriminant analysis in the case of a real E-SAR image. ©2010 IEEE.

关键词： Feature extraction

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Lidar-Visual-Inertial Odometry Using Point and Line Features 4

Lidar-Visual-Inertial Odometry Using Point and Line Features

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4th International Conference on robotics and Computer Vision, ICRCV 2022

作者： Huang, Ji Zhang, Yanduo Li, Xun Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot School of Computer Science and Engineering Wuhan China

ISBN: (数字)9781665481700

ISBN: (纸本)9781665481700

We present a robust real-time Lidar-Visual-Inertial Odometry method that introduces visual line features. Compared with the traditional multi-sensor fusion scheme based on point features, our approach introduces visual line features for pose estimation, utilizes more environmental features, and provides additional constraints on the scene structure. In our method, we first use an improved LSD algorithm for line feature extraction to achieve its real-time requirement, then extract the depth information of the point-line features by LIDAR. The extracted depth information serves as an a priori constraint for point-line bundle adjustment, and we finally use the computed pose estimation as an a priori estimation for the laser odometry to calculate the ultimate pose estimation, thus realizing a laser vision inertial odometry method using point-line features. This method can improve the accuracy of attitude estimation to some extent. Our evaluation of the M2DGR dataset shows that our method achieves higher accuracy than most open source frameworks. © 2022 IEEE.

关键词： Optical radar

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Improving flash translation layer performance by using log block mapping scheme and two-level buffer for address translation information 19

Improving flash translation layer performance by using log b...

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2019 International Conference on robotics Systems and Vehicle technology, RSVT 2019

作者： Xu, Yinxia School of Computer Science and Engineering Wuhan Institute of Technology Hubei Provincial Key Laboratory of Intelligent Robot China

ISBN: (纸本)9781450362429

In the era of big data, the requirement of mass storage and fast access of data makes solid state disk(SSD) based on NAND flash be widely used. However, increasing flash memory capacity imposes huge SRAM consumption for logical-physical translation table in a page-level flash translation layer(FTL). Existing FTL schemes selectively cache the on-demand address mappings to quicken the address translation, while keeping all address mappings in flash memory. But the page-level catching mechanism causes a certain degree of cache pollution. In this paper, we manage page-level address translation information at hybrid-level mapping scheme and use two-level buffer for map groups to decrease SRAM consumption while reducing the cache pollution. What’s more, an efficient replacement policy is designed. We can increase the cache hit ratio and reduce the write backs of evicted dirty entries and decrease garbage collection operations by these means. The performance and lifetime of the flash memory is improved. Experimental results show that the proposed scheme increases cache hit ratio by up to 28% and decreases the average response time by up to 23% compared with the existing FTL schemes. © 2019 Association for Computing Machinery.

关键词： Mapping

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Bimanual gesture recognition based on convolution neural network

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International Journal of Wireless and Mobile Computing 2020年第4期18卷 311-319页

作者： Wu, Hao Li, Gongfa Sun, Ying Jiang, Guozhang Jiang, Du Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan Hubei China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan Hubei China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan Hubei China

Gesture recognition is a key research field in the human-computer interaction. At present, most of researchers focus on one-handed gesture recognition, but do not pay much attention to bimanual (two hands) gesture recognition. This paper presents a deep learning-based solution to tackle the self-occlusion and self-similarity. To solve this problem, this paper uses Kinect to collect many colour and depth images of different gestures, and each gesture contains multiple sample individuals. Colour images and depth images are used to train the recognition model of bimanual gesture respectively, and then the colour image and depth image are fused, and the bimanual gesture recognition model is trained based on colour image and depth image fusion. Then, the bimanual recognition effects of the three models are compared. The experimental results show that, regardless of the single gesture precision or the mean average precision, the bimanual gesture recognition effect of the fused model is better than the gesture recognition models based on either colour image or depth image. Copyright © 2020 Inderscience Enterprises Ltd.

关键词： Deep learning

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Helmet wear detection based on EfficientNet-Y 5

Helmet wear detection based on EfficientNet-Y

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5th International Conference on Advanced Electronic Materials, Computers and Software Engineering, AEMCSE 2022

作者： Liu, Jie Liu, Lizhi Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology China Wuhan Institute of Technology School of Computer Science and Engineering China

ISBN: (数字)9781665484749

ISBN: (纸本)9781665484749

In response to government policies, "Internet +"has been integrated into the construction site to build a "smart site"ecosystem, and construction departments have begun to carry out visual management of the project. To address the problems of low recognition rate, slow detection speed, high hardware cost and complex construction site background for helmet wearing detection at construction sites, a lightweight model EfficientNet-Y is proposed in order to improve the detection accuracy, enhance the detection speed. The model uses EfficientNet backbone feature extraction network to replace the original. The experimental results demonstrate that the number of parameters of EfficientNet-Y model is reduced by 80% compared with the YOLOv3 model, and the mAP is increased by 1.5% compared with that of EfficientDet. The FPS is improved by 55% compared with YOLOv3 and doubled compared with EfficientDet, while the size of the model is only 1/4 of the volume size of YOLOv3 model. The newly constructed dataset resulted in a significant improvement in mAP for each model, with EfficientNet-Y improving by 7.95%, EfficientDet by 9.58%, and YOLOv3 by 5.01%. © 2022 IEEE.

关键词： Project management

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Detection of Wheat Protein Content Based on Near-Infrared Spectroscopy with Autoencoder and Stacking 3

Detection of Wheat Protein Content Based on Near-Infrared Sp...

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3rd International Conference on Artificial Intelligence and Computer Information technology, AICIT 2024

作者： Kong, Wenjing Liu, Jun Luo, Yuan Wuhan Institute of Technology School of Computer Science & Engineering Wuhan Institute of Technology Hubei Key Laboratory of Intelligent Robot Wuhan China

ISBN: (纸本)9798350365443

Accurately detecting the protein content in wheat is crucial for enhancing its nutritional value, optimizing food processing quality, and improving agricultural resource management. This study addresses the rapid and accurate detection of wheat protein content by constructing a regression model based on near-infrared (NIR) spectroscopy, combining an autoencoder with stacked ensemble learning. First, Savitzky-Golay filtering was employed to preprocess the raw spectral data, effectively removing noise and improving data quality. Subsequently, an autoencoder was introduced to efficiently reduce the dimensionality of the complex high-dimensional spectral features, preserving essential information while significantly simplifying model complexity and enhancing prediction efficiency and accuracy. Building on this, a stacked ensemble learning strategy was applied to integrate the predictive advantages of multiple base models, accurately capturing the nonlinear relationships within the spectral data. Experimental results demonstrated the superior performance of this model in predicting wheat protein content, achieving an R2 of 0.9766 and an RMSE of just 0.2529. Compared to traditional algorithms, the proposed model exhibited stronger generalization ability and robustness, providing robust technical support for wheat quality detection. © 2024 IEEE.

关键词： Near infrared spectroscopy

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