检索结果-内蒙古大学图书馆

2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi Xu, Xiong School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Lane line recognition plays an important role in road safety and intelligent transport system. This paper focuses on the key technology of lane line recognition, which is divided into three parts: firstly, the original image is preprocessed, including de-distortion and grey scale, to reduce noise and interference. Then, lane line information extraction is explored in terms of colour features and edge detection. By analysing the RGB and HSV colour modes and their conversion, the colour features are used to improve the accuracy and robustness of recognition. Meanwhile, the lane line edges are extracted by combining the improved Canny algorithm and commonly used edge detection operators, and the methods of threshold segmentation and Hough transform are applied to further describe the geometric features of lane lines. The direction of vehicle motion is determined by calculating the polar coordinate equation and vanishing point coordinates. Finally, this paper verifies the lane line recognition algorithm based on HSV-improved Canny-Hough through simulation experiments, and proves that it is more effective and accurate in lane line recognition compared with traditional methods. © 2025 SPIE.

关键词： Edge detection

来源：评论

学校读者我要写书评

暂无评论

Traffic sign recognition system based on YOLOv8-ConvNeXt

Traffic sign recognition system based on YOLOv8-ConvNeXt

引用

2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Fan, Chubin School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

In the context of road traffic safety, intelligent driver assistance systems are particularly important. During everyday driving, drivers are influenced by factors such as weather and fatigue, which can prevent them from quickly and accurately reacting to current traffic sign information. Therefore, to enable faster and more precise recognition, this paper designs an efficient traffic sign detection method. Image enhancement technology, particularly the Retinex algorithm, is introduced to improve the visual effect of images and enhance the separability of features. The introduction of the ConvNeXt V2 network further improves the detection speed and accuracy of the YOLOv8 model. Additionally, a self-collected dataset was supplemented to lay a good foundation for subsequent training. © 2025 SPIE.

关键词： Road and street markings

来源：评论

学校读者我要写书评

暂无评论

An intelligent interactive system based on LLM to combine CAD API development with FEA

An intelligent interactive system based on LLM to combine CA...

引用

2024 8th International Conference on Electrical, Mechanical and Computer engineering (ICEMCE)

作者： Mengqiang Yu Yujie Sheng Zhongxu Wan Shuyu Yang Muping Sun Hongxia Cai School of Mechatronic Engineering and automation Shanghai University Shanghai China Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University Shanghai China School of Communication & Information Engineering Shanghai University Shanghai China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

This paper presents an intelligent interactive system based on large language model (LLM). It aims to simplify the operation flow of CAD and FEA software and improve the efficiency of design and simulation analysis. The system uses C# and Python to develop CAD Secondarily and access LLM API locally. Build a professional database and implement the interaction between CAD and LLM. The natural language dialogue between the user and the CAD software is realized, and the design guidance and optimization suggestions are provided. Taking the design of the end mechanism of fruit picking robot as an example, the application effect of this system is demonstrated. The results of the case show that the system can effectively improve the efficiency of design and simulation analysis, and improve the design quality, and has a broad application prospect.

关键词： Electric potential Databases Terminology Interactive systems Large language models Software Natural language processing Robots Optimization Python

来源：评论

学校读者我要写书评

暂无评论

The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

引用

IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

来源：评论

学校读者我要写书评

暂无评论

Fruit classification and quality detection system based on improved YOLOv8 model

Fruit classification and quality detection system based on i...

引用

2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Huang, Yukun School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

With the expansion of fruit planting scale, the inefficiency of manual labor has gradually become a bottleneck limiting the development of the industry. In recent years, with the advancement of technology, research into modern intelligent agricultural technologies has also become a research hotspot. This paper is based on the YOLOv8 model for fruit classification and quality detection. First, image data enhancement is performed using operations such as power law transformation, and random operations like translation, flipping, and scaling are applied to augment the image dataset. A dataset is then established. Next, EfficientViT is introduced to replace the backbone network, and the SA attention mechanism is incorporated into the detection head, making the model more accurate in fruit classification and quality detection. Finally, a GUI (Graphical User Interface) visualization design is implemented to achieve a user-friendly interaction and is applied in the overall system design. © 2025 SPIE.

关键词： Graphical user interfaces

来源：评论

学校读者我要写书评

暂无评论

Research on fruit 3D point cloud segmentation based on improved YOLOv8

Research on fruit 3D point cloud segmentation based on impro...

引用

2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi Wang, Qingtang School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

With the development and progress of science and technology in our country, the continuous improvement of people's living standards, the planting area of fruits is constantly expanding, and the fruit yield is also constantly increasing, resulting in a shortage of labor resources and bringing huge challenges to fruit picking;At the same time, due to the impact of different geographical environments, climates, and other natural environments, as well as the mutual occlusion of fruits and leaves, the positioning and picking of fruits are affected, requiring a large amount of manpower to complete the picking operation. This article uses the improved YOLOv8 algorithm for instance segmentation of fruits to improve the accuracy and efficiency of instance segmentation. By extracting mask information, corresponding depth information is obtained, and the point cloud data corresponding to apples is obtained, providing assurance for the accuracy of subsequent localization. Firstly, in the backbone network, the deformable convolutional DCNv2 is replaced with the C2f module to increase attention to small targets;By adding an improved ECA attention mechanism EMCA before the Neck layer, the model efficiency is improved. Finally, by proposing WPDIoU instead of CIoU to improve the loss, the convergence speed and segmentation accuracy of the model are improved. After experimental comparison between the original YOLOv8 model and the improved YOLOv8 model proposed in this paper, the results show that the improved YOLOv8 model has higher instance segmentation accuracy, an increase of 4.2 percentage points, and has a certain improvement in fruit instance segmentation accuracy. © 2025 SPIE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Animal image recognition system based on Yolov8-AFPN

Animal image recognition system based on Yolov8-AFPN

引用

2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Li, Ning School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Object detection technology has been widely applied in the conservation and monitoring of wildlife in recent years. However, due to the complexity of field environments, the collected images of wild animals often suffer from severe lighting effects, partial blurriness, and significant environmental differences. These issues greatly impact the efficiency and accuracy of object recognition. To overcome these challenges, this paper employs Gaussian filtering as an image enhancement technique and introduces an Asymptotic Feature Pyramid Network (AFPN) to improve the YOLOv8 model. The AFPN extracts features from the bottom up and uses adaptive spatial fusion operations to optimize the feature fusion process, significantly enhancing the detection performance of animal image recognition models. © 2025 SPIE.

关键词： Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Study on gesture recognition based on improved YOLOV8

Study on gesture recognition based on improved YOLOV8

引用

2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi Zhang, Hengrui School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Based on YOLOV8 hand gesture signal recognition in today's hand gesture signal recognition field has a great development and application prospects, which improve the detection accuracy of the hand gesture signal is the most important thing. Hand gesture signal recognition and detection and other areas of target detection is slightly different, hand gesture in the whole picture often belongs to a smaller target, and hand gesture signal recognition of complex application scenarios, In an effort to enhance the detection precision of hand gesture signal, this paper in initial YOLOV8 algorithm based on YOLOV8 algorithm: in YOLOV8 backbone network tail to join the Multi-Scale Convolutional Attention (MSCA) module is added to the tail of YOLOV8 backbone network;Large Selective Kernel Network (LSKNet) module is included into the front of YOLOV8 detection head;and the loss function of YOLOV8 is exchanged for Wise-IoU. The improved YOLOV8 is evaluated against initial YOLOV8 in control experiments, and the experimental results indicate that all the exactness measurements of YOLOV8 are enhanced in the detection and identification of hand gesture signal, and it can be proved that this paper's algorithm can enhance the exactness of YOLOV8. The experimental conclusions indicate that the improved YOLOV8 has enhanced all the accuracy measures in the recognition and detection of hand gesture signal, and it can be demonstrated that the improved YOLOV8 suggested in this paper has certain effect in the recognition and detection of hand gesture signal. © 2025 SPIE.

关键词： Palmprint recognition

来源：评论

学校读者我要写书评

暂无评论

Research on surface reconstruction algorithm based on upsampling fusion α-shape

Research on surface reconstruction algorithm based on upsamp...

引用

2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Wang, Shiyi He, Xinyu School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

The living standards and quality of life of the Chinese population have been steadily improving. Farmers increasingly seek non-invasive methods to obtain physical indicators of livestock to avoid stress-induced reactions. Similarly, animal welfare professionals aim to measure the physical characteristics of animals without causing them any disturbance. This study leverages point cloud reconstruction technology combined with machine learning techniques to measure the body features of animals. Using a Kinect depth vision camera, point cloud data of pigs is captured and reconstructed. The processed point cloud data is upsampled, and a surface reconstruction algorithm based on upsampling fusion and α-shape is proposed. The pig's volume is calculated using a convex hull algorithm, while its surface area is determined using PCL (Point Cloud Library) functions. © 2025 SPIE.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

SLAM semantic map construction based on improved YOLOv8

SLAM semantic map construction based on improved YOLOv8

引用

2024 International Conference on Mechatronic engineering and Artificial Intelligence, MEAI 2024

作者： Qin, Lijuan Tang, Xiaoyu Wang, Xiaohan School of Information Science and Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781510689176

Utilized SLAM (Simulated Localization And Mapping) technology for simultaneous localization and map construction, as well as relevant theoretical methods based on deep learning, to construct semantic maps. Studied a visual SLAM algorithm based on depth cameras to address the current issue of ground robots not being able to obtain effective real-time semantic information in the environment. In order to further enhance the real-time modeling and environmental awareness capabilities of the SLAM system, I have improved YOLOv8 to enable more accurate semantic segmentation. Replace the feature extraction network of YOLOv8 with ConvNeXt V2 network, effectively processing non masked (visible) pixels in the input image. The hierarchical structure of its encoder also helps to capture feature information at different levels. At the same time, change the original loss function CIoU to EfficiCIoU Loss. EfficiCIoU Loss can be more refined and accurate in refining loss calculations compared to CIoU, and a more effective loss function can help the model converge faster. © 2025 SPIE.

关键词： Semantic Segmentation

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：