International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)最新文献_第10页

Combinatorial action recognition based on causal segment intervention 基于因果片段干预的组合动作识别

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014465

Xiaozhou Sun

引用次数: 0

The ship classification and detection method of optical remote sensing image based on improved YOLOv7-tiny 基于改进型 YOLOv7-tiny 的光学遥感图像的船舶分类和探测方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014371

Jinwei Cheng, Jie Yuan, Xiaoning Hu, Baorong Xie, Junrui Wang

{"title":"The ship classification and detection method of optical remote sensing image based on improved YOLOv7-tiny","authors":"Jinwei Cheng, Jie Yuan, Xiaoning Hu, Baorong Xie, Junrui Wang","doi":"10.1117/12.3014371","DOIUrl":"https://doi.org/10.1117/12.3014371","url":null,"abstract":"In view of the fault and leak detection problems caused by complex scenes of offshore area in remote sensing image ship detection, a lightweight ship classification detection method is proposed based on improved YOLOv7-tiny. On the one hand, this method stacks a lightweight feature extraction module and applies it to the backbone feature extraction network, which significantly reduces the parameter and computational complexity and does not weaken the network's ability of feature extraction. On the other hand, this method introduces spatial information into the feature pyramid, raising the discrimination of features at different scales, to improve the classification and detection ability of the network. This method has been tested on the remote sensing image ship data set. The experimental results show that the average accuracy of ship classification detection based on the improved network is increased by 2.9%. Meanwhile, the parameter quantity and computational complexity are better than YOLOv7-tiny, with a 15% reduction in parameter quantity and a 24% reduction in computational complexity.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":" 80","pages":"129691F - 129691F-9"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improved ant colony algorithm based on artificial gravity field for adaptive dynamic path planning 基于人工重力场的改进型蚁群算法，用于自适应动态路径规划

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014563

Shuo Wang, Lutao Yan, Haiyuan Li, Jian Li

{"title":"Improved ant colony algorithm based on artificial gravity field for adaptive dynamic path planning","authors":"Shuo Wang, Lutao Yan, Haiyuan Li, Jian Li","doi":"10.1117/12.3014563","DOIUrl":"https://doi.org/10.1117/12.3014563","url":null,"abstract":"In view of the problems such as unclear target direction, low search efficiency, and slow convergence speed of the basic ant colony algorithm in AGV two-dimensional path planning, an improved ant colony algorithm based on artificial gravity field and triangle pruning method is proposed. The algorithm first uses the attractive strength provided by the gravity field to construct heuristic information, enhancing the guidance of the target point on the planning direction and improving the directionality and search efficiency. Then, based on the concentration enhancement mechanism of the elite ant model's pheromone, an adaptive reward update mechanism for increments is proposed to improve the convergence speed. Next, an adaptive adjustment mechanism of the pheromone heuristic factor value correlated with the iteration number is discussed to balance the randomness and search efficiency of the entire planning process. Finally, the triangle pruning method is applied to global path optimization based on global path planning, effectively reducing the number of turning nodes and improving the actual motion efficiency. Comparative experiments on path planning in two-dimensional static maps using matlab validate the effectiveness of the improved algorithm in AGV global dynamic path planning.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":" 38","pages":"129690X - 129690X-10"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139640392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development and application of virtual synchronization system for live working robots based on binocular vision 基于双目视觉的现场工作机器人虚拟同步系统的开发与应用

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014626

JunHui Yan, JianFang Shen

引用次数: 0

Research on rule engine optimization algorithm in internet of things teaching platform 物联网教学平台中的规则引擎优化算法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014592

JianZhong Li, Qiang Wan, ZhiQiang Zhang

引用次数: 0

Research on 3D reconstruction algorithms for small scenes with weak texture 弱纹理小场景三维重建算法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014370

Changrui Nai

引用次数: 0

Towards Huanglongbing In-field detection system with AI edge computing 利用人工智能边缘计算开发黄龙兵现场检测系统

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014425

Xuefeng Rao, Quanyou Zhao, Dingming Huang

引用次数: 0

Research on intelligent design algorithm of indoor space based on hybrid recommendation model 基于混合推荐模型的室内空间智能设计算法研究

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014657

Huaxue He

{"title":"Research on intelligent design algorithm of indoor space based on hybrid recommendation model","authors":"Huaxue He","doi":"10.1117/12.3014657","DOIUrl":"https://doi.org/10.1117/12.3014657","url":null,"abstract":"Looking at the traditional interior space design industry, the traditional design method is mainly manual design and the use of interactive modeling software and its design process mainly relies on trial and error. This paper takes the interior space design software platform as the background to study the collocation recommendation algorithm of the 3D home model, aim at improve the efficiency of the intelligent design algorithm. The recommendation idea of collaborative filtering is simple to implement, does not need to consider the inherent attribute characteristics of three-dimensional home projects, and is fast to calculate. After constructing the image feature database, this article uses the similarity between images to measure the visual similarity of the indoor space model; uses similar home projects to predict the collocation data of adjacent projects, and densifies the sparse collocation data; constructs each image separately Feature database, and use this to build its similarity table. According to the similarity table corresponding to each item, the first simNum items of the same category that are similar to the current item can be found. The experimental results show that compared with the traditional algorithm, the algorithm in this paper has greatly improved the accuracy of collocation recommendation.","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"28 4","pages":"129690B - 129690B-6"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised deep learning image stitching model assisted with infrared images 辅助红外图像的无监督深度学习图像拼接模型

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014359

Ming Zhu, Chengkun Li, Xueying He, Xiao Xiao

{"title":"Unsupervised deep learning image stitching model assisted with infrared images","authors":"Ming Zhu, Chengkun Li, Xueying He, Xiao Xiao","doi":"10.1117/12.3014359","DOIUrl":"https://doi.org/10.1117/12.3014359","url":null,"abstract":"The rapid development of artificial intelligence facilitates the improvement of image processing algorithms. For an intelligent inspection robot, the ability to analyze the environment through image collection plays an important role. It needs to collect multiple images of the same scene from different angles of view so as to make a thorough analysis about the environment it locates and generate further decisions. Therefore, a technique called image stitching is used. Currently, the development of image stitching algorithms is getting mature – multiple algorithms have already been proposed based feature extraction techniques. However, these existing algorithms are usually unable to handle the problem of parallax existing in real world image. Therefore, in order to solve it, we proposed an unsupervised deep learning image stitching algorithm, which uses infrared images to provide auxiliary information. We utilized our own equipment to collect real world images in visible light and infrared. Finally, we implemented our own model and other popular existing image stitching algorithms and compared and contrasted their performance on our dataset. The results showed that our model has the best performance in all aspects than other algorithms on the dataset, indicating the strong advantages of deep learning methods on image stitching tasks","PeriodicalId":516634,"journal":{"name":"International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023)","volume":"12 1","pages":"1296914 - 1296914-7"},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140511400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A grammar-based layout method for graph models 基于语法的图模型布局方法

International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023) Pub Date : 2024-01-09 DOI: 10.1117/12.3014367

Yufeng Liu, Yang Zhou, Fan Yang, Song Li

引用次数: 0