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A Lightweight Road Scene Semantic Segmentation Algorithm

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Computers, Materials & Continua 2023年第11期77卷 1929-1948页

作者： jiansheng peng Qing Yang Yaru Hou College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi547000China

In recent years,with the continuous deepening of smart city construction,there have been significant changes and improvements in the field of intelligent *** semantic segmentation of road scenes has important practical significance in the fields of automatic driving,transportation planning,and intelligent transportation ***,the current mainstream lightweight semantic segmentation models in road scene segmentation face problems such as poor segmentation performance of small targets and insufficient refinement of segmentation ***,this article proposes a lightweight semantic segmentation model based on the LiteSeg model improvement to address these *** model uses the lightweight backbone network MobileNet instead of the LiteSeg backbone network to reduce the network parameters and computation,and combines the Coordinate Attention(CA)mechanism to help the network capture long-distance *** the same time,by combining the dependencies of spatial information and channel information,the Spatial and Channel Network(SCNet)attention mechanism is proposed to improve the feature extraction ability of the ***,a multiscale transposed attention encoding(MTAE)module was proposed to obtain features of different resolutions and perform feature *** this paper,the proposed model is verified on the Cityscapes *** experimental results show that the addition of SCNet and MTAE modules increases the mean Intersection over Union(mIoU)of the original LiteSeg model by 4.69%.On this basis,the backbone network is replaced with MobileNet,and the CA model is added at the same *** the cost of increasing the minimum model parameters and computing costs,the mIoU of the original LiteSeg model is increased by 2.46%.This article also compares the proposed model with some current lightweight semantic segmentation models,and experiments show that the comprehensive performance of the proposed model is the best,especially in

关键词： Semantic segmentation lightweight road scenes multi-scale transposition attention encoding(MTAE)

Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review

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Computers, Materials & Continua 2020年第9期64卷 1977-1997页

作者： Kui Fu jiansheng peng Hanxiao Zhang Xiaoliang Wang Frank Jiang School of Physics and Mechanical and Electronic Engineering Hechi UniversityYizhou546300China School of Electrical and Information Engineering Guangxi University of Science and TechnologyLiuzhou545006China School of Computer Science and Engineering Hunan University of Science and TechnologyXiangtan411100China School of Information Technology Deakin UniversityGeelongVIC 3220Australia

Single image super resolution(SISR)is an important research content in the field of computer vision and image *** the rapid development of deep neural networks,different image super-resolution models have *** to some traditional SISR methods,deep learning-based methods can complete the super-resolution tasks through a single *** addition,compared with the SISR methods using traditional convolutional neural networks,SISR based on generative adversarial networks(GAN)has achieved the most advanced visual *** this review,we first explore the challenges faced by SISR and introduce some common datasets and evaluation ***,we review the improved network structures and loss functions of GAN-based perceptual ***,the advantages and disadvantages of different networks are analyzed by multiple comparative ***,we summarize the paper and look forward to the future development trends of GAN-based perceptual SISR.

关键词： Single image super-resolution generative adversarial networks deep learning computer vision

Improved Double Deep Q Network Algorithm Based on Average Q-Value Estimation and Reward Redistribution for Robot Path Planning

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Computers, Materials & Continua 2024年第11期81卷 2769-2790页

作者： Yameng Yin Lieping Zhang Xiaoxu Shi Yilin Wang jiansheng peng Jianchu Zou Key Laboratory of Advanced Manufacturing and Automation Technology Guilin University of TechnologyEducation Department of Guangxi Zhuang Autonomous RegionGuilin541006China Guangxi Key Laboratory of Special Engineering Equipment and Control Guilin University of Aerospace TechnologyGuilin541004China Guilin Mingfu Robot Technology Company Limited Guilin541199China Key Laboratory of AI and Information Processing Education Department of Guangxi Zhuang Autonomous RegionHechi UniversityYizhou546300China

By integrating deep neural networks with reinforcement learning,the Double Deep Q Network(DDQN)algorithm overcomes the limitations of Q-learning in handling continuous spaces and is widely applied in the path planning of mobile ***,the traditional DDQN algorithm suffers from sparse rewards and inefficient utilization of high-quality *** those problems,an improved DDQN algorithm based on average Q-value estimation and reward redistribution was ***,to enhance the precision of the target Q-value,the average of multiple previously learned Q-values from the target Q network is used to replace the single Q-value from the current target Q ***,a reward redistribution mechanism is designed to overcome the sparse reward problem by adjusting the final reward of each action using the round reward from trajectory ***,a reward-prioritized experience selection method is introduced,which ranks experience samples according to reward values to ensure frequent utilization of high-quality ***,simulation experiments are conducted to verify the effectiveness of the proposed algorithm in fixed-position scenario and random *** experimental results show that compared to the traditional DDQN algorithm,the proposed algorithm achieves shorter average running time,higher average return and fewer average *** performance of the proposed algorithm is improved by 11.43%in the fixed scenario and 8.33%in random *** not only plans economic and safe paths but also significantly improves efficiency and generalization in path planning,making it suitable for widespread application in autonomous navigation and industrial automation.

关键词： Double Deep Q Network path planning average Q-value estimation reward redistribution mechanism reward-prioritized experience selection method

Dynamic SLAM Visual Odometry Based on Instance Segmentation:A Comprehensive Review

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Computers, Materials & Continua 2024年第1期78卷 167-196页

作者： jiansheng peng Qing Yang Dunhua Chen Chengjun Yang Yong Xu Yong Qin College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi547000China

Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous ***,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map *** the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic *** to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s *** article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and *** comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic ***,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM *** recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance ***,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.

关键词： Dynamic SLAM instance segmentation visual odometry

Multi-Robot Privacy-Preserving Algorithms Based on Federated Learning:A Review

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Computers, Materials & Continua 2023年第12期77卷 2971-2994页

作者： jiansheng peng Jinsong Guo Fengbo Bao Chengjun Yang Yong Xu Yong Qin College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi547000China

The robotics industry has seen rapid development in recent years due to the Corona Virus Disease *** the development of sensors and smart devices,factories and enterprises have accumulated a large amount of data in their daily production,which creates extremely favorable conditions for robots to perform machine ***,in recent years,people’s awareness of data privacy has been increasing,leading to the inability to circulate data between different enterprises,resulting in the emergence of data *** emergence of federated learning provides a feasible solution to this problem,and the combination of federated learning and multi-robot systems can break down data silos and improve the overall performance of ***,as scholars have studied more deeply,they found that federated learning has very limited privacy ***,how to protect data privacy from infringement remains an important *** this paper,we first give a brief introduction to the current development of multi-robot and federated learning;second,we review three aspects of privacy protection methods commonly used,privacy protection methods for multi-robot,and Other Problems Faced by Multi-robot Systems,focusing on method comparisons and challenges;and finally draw conclusions and predict possible future research directions.

关键词： Federated learning multi-robot privacy protection gradient leakage attacks

RRT Autonomous Detection Algorithm Based on Multiple Pilot Point Bias Strategy and Karto SLAM Algorithm

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Computers, Materials & Continua 2024年第2期78卷 2111-2136页

作者： Lieping Zhang Xiaoxu Shi Liu Tang Yilin Wang jiansheng peng Jianchu Zou Key Laboratory of Advanced Manufacturing and Automation Technology(Guilin University of Technology) Education Department of Guangxi Zhuang Autonomous RegionGuilin541006China College of Mechanical and Control Engineering Guilin University of TechnologyGuilin541006China Guilin Mingfu Robot Technology Company Limited Guilin541199China Key Laboratory of AI and Information Processing(Hechi University) Education Department of Guangxi Zhuang Autonomous RegionYizhou546300China

A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile ***,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random *** that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT *** algorithm simulation platform based on the Gazebo platform was *** simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more ***,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different *** experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.

关键词： Autonomous detection RRT algorithm mobile robot ROS Karto SLAM algorithm

Visual SLAM Based on Object Detection Network:A Review

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Computers, Materials & Continua 2023年第12期77卷 3209-3236页

作者： jiansheng peng Dunhua Chen Qing Yang Chengjun Yang Yong Xu Yong Qin College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of Artificial Intelligence and Manufacturing Hechi UniversityHechi547000China

Visual simultaneous localization and mapping(SLAM)is crucial in robotics and autonomous ***,traditional visual SLAM faces challenges in dynamic *** address this issue,researchers have proposed semantic SLAM,which combines object detection,semantic segmentation,instance segmentation,and visual *** the growing body of literature on semantic SLAM,there is currently a lack of comprehensive research on the integration of object detection and visual ***,this study aims to gather information from multiple databases and review relevant literature using specific *** focuses on visual SLAM based on object detection,covering different ***,it discusses the current research status and challenges in this field,highlighting methods for incorporating semantic information from object detection networks into mileage measurement,closed-loop detection,and map *** also compares the characteristics and performance of various visual SLAM object detection ***,it provides an outlook on future research directions and emerging trends in visual *** has shown that visual SLAM based on object detection has significant improvements compared to traditional SLAM in dynamic point removal,data association,point cloud segmentation,and other *** can improve the robustness and accuracy of the entire SLAM system and can run in real *** the continuous optimization of algorithms and the improvement of hardware level,object visual SLAM has great potential for development.

关键词： Object detection visual SLAM visual odometry loop closure detection semantic map

基于Symbian平台智能家居控制系统的设计与实现

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天津师范大学学报（自然科学版） 2011年第2期31卷 55-58页

作者：彭建盛河池学院物理与电子工程系广西宜州546300

基于Symbian手机平台设计了一套智能家居控制系统,利用Symbian系统提供的各种资源建立智能家居远程端控制软件,并设计了一个安装在家庭的家居服务控制器.通过GSM网络,智能手机软件与家居服务器可以进行通信,实现远程控制家居设备和监控... 详细信息

基于Symbian手机平台设计了一套智能家居控制系统,利用Symbian系统提供的各种资源建立智能家居远程端控制软件,并设计了一个安装在家庭的家居服务控制器.通过GSM网络,智能手机软件与家居服务器可以进行通信,实现远程控制家居设备和监控家居环境的功能.设计结果表明:通过智能手机软件控制智能家居成为可能,系统以一种新的智能家居控制方式让家居生活就在手边.

关键词： Symbian 智能手机智能家居

The Electronics Experiment Teaching Reform Based on the CDIO

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The Electronics Experiment Teaching Reform Based on the CDIO