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SSD object detection algorithm based on knowledge map

International Journal of Wireless and Mobile Computing 2024年第2期27卷 152-160页

作者： Huang, Li Wang, Xiaofeng Lu, Jianhua Hu, Wei Zhang, Changrong College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the pervasive integration of artificial intelligence into all aspects of human life, talent emerges as a primary resource. Upon analysing the current state of talent training in higher education institutions, issues such as dispersed knowledge points, overlapping content, a singular practice approach and ineffective evaluation have been identified. In response to these challenges, this paper proposes a multidisciplinary and comprehensive practical teaching methodology grounded in the knowledge graph framework. It delves into diverse paths for practical teaching and assessment, including aspects like teaching objectives, problem decomposition, resource integration, implementation methods and performance evaluation. The practical application of the SSD algorithm in researching service robot indoor object detection serves as an illustrative example. Employing the holistic practical teaching approach facilitated by the knowledge graph, this model guides students in acquiring object detection expertise, thereby enhancing their comprehensive development. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Object detection

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Fruit target detection method based on faster R-CNN

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International Journal of Wireless and Mobile Computing 2021年第3期21卷 207-213页

作者： Yin, Guanghui Xie, Yuanmin Yun, Juntong Ning, Lichuan Liu, Ying Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Hubei Provincial Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China

With the rapid development of agricultural modernisation, fruit picking is becoming more and more automatic. The detection of fruit target by machine vision technology is the key to realise fruit automatic picking. In recent years, with the development of deep learning technology, target detection algorithm based on deep learning has gradually become a hot research topic, and the detection accuracy has been greatly improved. However, the shape and size of fruits in natural environment are different, and the light intensity changes at any time, which affects the detection accuracy to a certain extent. In this paper, aiming at the problem of fruit detection and location in natural environment, based on Fast R-CNN target detection model, a fruit detection and location method combining image processing and deep learning is proposed. The experimental results show that the combination of image processing and deep learning can achieve high detection accuracy and speed. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Fruits

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Eye state detection in facial image based on linear prediction error of wavelet coefficients

Eye state detection in facial image based on linear predicti...

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2008 IEEE International Conference on robotics and biomimetics, ROBIO 2008

作者： Cheng, Erkang Kong, Bin Hu, Rongxiang Zheng, Fei Center for Biomimetic Sensing and Control Research Institute of Intelligent Machine Chinese Academy of Science Hefei Anhui Province China Department of Automation University of Science and Technology of China Hefei Anhui Province China Key Laboratory of Biomimetic Sensing and Advanced Robot Technology Anhui Province China

ISBN: (纸本)9781424426799

Eye state detection in facial image is a significant issue in face recognition, human-computer interface and driver fatigue monitoring system. In this paper, we first located the eye region in the upper area of the face region with AbaBoost algorithm. The linear predictor error distribution of wavelet coefficients was proposed as the statistics model to distinguish the eye states. We collected statistics (mean, variance, skewness, and kurtosis) of the prediction error distribution as eye state features. Build on these eye state features and support machine vector (SVM) with radial basis function (RBF) kernel a non-linear classifier is obtained by training samples of eye images. Experiment results with the classifier demonstrated that our method is an effective eye state detection approach which can satisfy various situations. © 2008 IEEE.

关键词： Face recognition

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Optimised design of cross-shaft parameters based on response surface optimisation model with MOGA

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International Journal of Wireless and Mobile Computing 2024年第2期26卷 198-206页

作者： Xiong, Sijie Xie, Yuanmin Zou, Chunlong Mao, Yanfeng Cao, Yongcheng Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Precision Manufacturing Research Institute Wuhan University of Science and Technology Hubei Wuhan China Hubei University of Automotive Technology Hubei Shiyan China Technology Research and Development Hubei Jingmen Wusan Machinery Equipment Manufacturing Co. Ltd. Jingmen Hubei China

Cross-shaft is the core component of the cross-type universal coupling and has a vital transmission function. This paper proposes Sparse Grid and the Kriging interpolation to construct a response surface model to solve the problem of long design cycles, low reliability and high susceptibility to cross-shaft fatigue deformation. The critical dimensions of the cross-shaft are used as design variables, and the maximum equivalent force and deformation are reduced as the optimisation objective. Then experimental points are obtained by Sparse Grid Initialisation and then the response surface model is obtained with high accuracy by Kriging interpolation, and finally, the optimised design of the cross-shaft is completed using MOGA in this paper. Compared with the original structural solution, the maximum deformation of the cross-shaft is reduced by 0.4717 mm (22.35%), the maximum equivalent force is reduced by 130.35 Mpa (17.21%) and the mass increased by only 4.17%. © 2024 Inderscience Enterprises Ltd.

关键词： Multiobjective optimization

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Simulation research on trajectory tracking control system of manipulator based on fuzzy PID control

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International Journal of Innovative Computing and Applications 2022年第5-6期13卷 269-277页

作者： Ma, Ruyi Li, Zeshen Jiang, Du Yun, Juntong Liu, Ying Liu, Yibo Bai, Dongxu Li, Gongfa Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Hubei Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan430081 China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan430081 China

In view of the influence of external environment interference, parameter changing and random noise in the process of manipulator trajectory tracking, as well as the problems of fuzzy PID control, such as fuzzy rules are not easy to set, quantisation interval and parameters are affected by subjective consciousness, which leads to the problem of insufficient performance of manipulator system. In this paper, firstly, in the MATLAB simulation environment, based on the D-H parameter model, the linkage model of the four degree of freedom manipulator is established, and the homogeneous transformation matrix of the manipulator is calculated. Then, through the application of fuzzy PID control in the Simulink toolbox, the optimal parameters of the controller are found by using the proportion factor self-adjusting strategy, and the corresponding trajectory tracking and forward inverse kinematics simulation are studied. The trajectory tracking curve of the manipulator is obtained. The research method and experimental results in the paper are of great significance for the simulation of articulated manipulator. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Simulink

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Object Detection with Depth Information in Road Scenes 8th

Object Detection with Depth Information in Road Scenes

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8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023

作者： Liu, Ruowang Chen, Xinbo Tao, Bo Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9789819980208

In recent years, depth estimation has witnessed significant advancements because of the development of deep learning. It's important to note that depth estimation tasks focus solely on predicting the depth of each pixel in an image and do not include object detection or object recognition. Depth estimation is the use of pixel transformations in the image to obtain distance information from each point in the scene to the camera to generate a depth map. Object detection is the process of classifying and localizing an image, given a picture, so as to identify the objects in the picture and determine their location. To overcome this limitation and integrate object detection into the depth estimation process, this paper proposes a novel self-supervised monocular depth estimation algorithm that leverages an attention mechanism. By combining object detection and depth estimation, a real-time multi-task model is designed to enable simultaneous detection and depth estimation of objects. The framework comprises four essential components: an object detection sub-network, a depth estimation sub-network, a lateral sharing unit, and an attention loss. These components work collaboratively to enhance distance estimation accuracy for objects and improve the object detection performance. Throughout experiments, it is evident that the proposed approach can effectively estimate distances to objects and enhances the accuracy of object detection. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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PINN-based neural network for photoelastic stress recovery

PINN-based neural network for photoelastic stress recovery

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2023 Advanced Fiber Laser Conference, AFL 2023

作者： Lin, Peng Tao, Bo Wang, Yan Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan China Precision Manufacturing Institute Wuhan University of Science and Technology Wuhan China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781510677661

Optical measurement techniques have emerged as a promising alternative to traditional invasive methods for stress recovery. Recent developments in artificial intelligence have enabled researchers to harness Physics-Informed Neural Networks (PINNs) for solving complex physics-based problems, including stress field recovery from photoelastic fringes. This paper explores the potential of PINNs for non-invasive stress analysis, presenting a novel methodology that integrates photoelastic fringes and physical stress distribution constraints within a deep learning framework. Our results demonstrate the model's robustness in handling diverse stress patterns and its ability to accurately recover stress distributions even when trained on significantly reduced datasets. A comparative analysis shows our PINN-based model's performance surpassing or closely matching existing methods like PhotoelastNet, underscoring its competitive advantage in stress recovery. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Stress concentration

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Multi-stream fusion network for continuous gesture recognition based on sEMG

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International Journal of Wireless and Mobile Computing 2024年第4期26卷 374-383页

作者： Li, Jun Zou, Chunlong Tang, Dalai Sun, Ying Fan, Hanwen Li, Boao Tang, Xinjie Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Hubei Wuhan China College of Mechanical Engineering Hubei University of Automotive Technology Hubei Shiyan China Inner Mongolia University of Finance and Economics Hohhot China Research Centre for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Hubei Wuhan China Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Hubei Wuhan China

Dynamic gesture recognition is an essential step in human-computer interaction. However, dynamic gesture recognition based on Surface Electromyography (sEMG) signals faces issues such as incomplete feature extraction and incomplete recognition of temporal information between continuous gestures. Therefore, this paper proposes a multi-stream fusion network (MSK-LCNN) for dynamic gesture recognition to improve accuracy. We combine CNN and LSTM models into a unified framework which extracts spatio-temporal information both globally and in depth, and combines feature fusion to retain essential information. This framework uses an attention mechanism (SKNet) to learn more intricate feature information. The gesture recognition accuracy of this method on our data set is 95.23%, and on the NinaPro DB1 data set, it is 91.45%, which outperforms other similar networks in recent years. Applying this algorithm to prosthetic hand control has achieved flexible and stable control of the prosthetic hand. © 2024 Inderscience Enterprises Ltd.

关键词： Gesture recognition

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Grip strength forecast and rehabilitative guidance based on adaptive neural fuzzy inference system using sEMG

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Personal and Ubiquitous Computing 2022年第4期26卷 1215-1224页

作者： Jiang, Du Li, Gongfa Sun, Ying Kong, Jianyi Tao, Bo Chen, Disi Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan University of Science and Technology Wuhan430081 China Institute of Precision Manufacturing Wuhan University of Science and Technology Wuhan University of Science and Technology Wuhan430081 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China School of Computing University of Portsmouth PortsmouthPO1 3HE United Kingdom

In order to resolve the problem of unstable control of force in human–computer interaction based on surface EMG signals, the adaptive neural fuzzy inference system is designed to achieve the grip strength assessment. As we know, the acquisition of surface EMG signal is non-invasive, which provides a better evaluation index for rehabilitation training in the medical process. By establishing the relationship between grip force and surface electromechanical signals, the effect of rehabilitation training can be evaluated directly while reducing the types of sensors used. Firstly, the experimental equipment are introduced, which are utilized to carry out simultaneous acquisition of surface EMG signals and forces. Then, the traditional features of sEMG and the corresponding algorithms are illustrated, based on this, supplementing the energy eigenvalue with wavelet analysis and fuzzy entropy. In which, fuzzy entropy is effective in characterizing muscle fatigue that can effectively reduce the impact of muscle fatigue on force assessment. Finally, combining fuzzy logic implication and neural network, the adaptive neural fuzzy inference system is designed, which is trained by extracted feature vectors. The experimental result shows the method used in this paper can effectively predict the grip force. Further, force prediction based on sEMG can be used to guide rehabilitation therapy in virtual space, combined with an electrical stimulator. © 2019, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Eigenvalues and eigenfunctions

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Multi-branch low-light enhancement algorithm based on spatial transformation

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Multimedia Tools and Applications 2024年 1-21页

作者： Wang, Wenlu Sun, Ying Zou, Chunlong Tang, Dalai Fang, Zifan Tao, Bo Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China School of Computer Information Management Inner Mongolia University of Finance and Economics Hohhot010051 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Precision Manufacturing Research Institute Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Hydroelectric Design & amp Maintenance China Three Gorges University Yichang443002 China

Many state-of-the-art low-light image enhancement techniques now suffer from issues like color distortion, detail blurring, and the halo effect, hindering their ability to produce visual effects. This paper presents a multi-branch low-light enhancement algorithm based on spatial transformation to significantly improve the visual impact of images under low-light settings, overcoming the limitations of existing approaches. The process involves dividing the initial low-light picture into three distinct layers. Bootstrap filtering and Contrast Limited Adaptive Histogram Equalization are used to process the main feature layer. Primary feature extraction is used to extract features for the compensation layer, which is then integrated with Contrast Limited Adaptive Histogram Equalization and MSRCR. The auxiliary layer utilizes spatial transformation to apply gamma correction to the V-channel and performs color space transformation and additional processing. Ultimately, these three layers are combined and refined to create the final improved image. Based on the experimental results, the proposed method outperforms the standard MSRCR algorithm by 52.47%, 71.58%, and 12.10%, respectively, on the assessment metrics AG, SD, and IE. LOE declines by 22.3%. The processed image presents a good visual quality, effectively enhancing the image brightness while retaining a large amount of information and improving the image details. This paper proposes an effective method to deal with the problem of low illumination image enhancement, which provides a strong reference for future research and application in related fields. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image enhancement

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