Improvement and Implementation of Machining and Positioning Method of Intelligent Construction Machinery Components Relying on Machine Vision

doi:10.38007/kme.2021.020206

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…5. The improved pixel gray histogram can judge whether the point cloud and the target gray histogram are equal or not [19]. Other methods for calculating point cloud images can be used to solve the problems of 3D point cloud images missing, low quality, and texture blur, and improve the matching accuracy of 3D point cloud images.…”

Section: Structural Scheme Design Of Unmanned Vehicle Fusion Position...mentioning

confidence: 99%

RETRACTED ARTICLE: Unmanned Vehicle Fusion Positioning Technology Based on “5G + Beidou” and 3D Point Cloud Image

Fu,

Zhao,

Fan

et al. 2023

Int J Comput Intell Syst

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Unmanned vehicles need to know their location and direction information accurately to plan and navigate their paths. However, the positioning system is susceptible to interference from a variety of factors, which leads to increased positioning errors, thereby affecting the accuracy of unmanned vehicle positioning. An unmanned vehicle fusion positioning technology based on the "5G + Beidou" integrated positioning system was proposed. While using the "5G + Beidou" base station for positioning, the 3D point cloud image was fused, and the high-precision real-time positioning was carried out through the vehicle's autonomous navigation algorithm. This paper first analyzed the current situation and characteristics of GNSS technology and studied the key technologies and principles of the "5G + Beidou" integrated positioning system. Then, aiming at the difficulty of 5G base station deployment, the GNSS system parameter optimization scheme based on a multidimensional fusion structure was designed. Finally, in the experiment, it was verified that the fusion system could achieve higher precision positioning results compared with traditional single-dimensional GNSS and multi-dimensional GNSS. The technical advantages of "5G + Beidou" were used for data fusion processing of unmanned vehicles, and a positioning method based on the combination of 3D point cloud image and high-precision map was proposed. Through some experiments, it was concluded that the fusion location method could control the error below 0.1, which showed the accuracy of the fusion location.

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Section: Structural Scheme Design Of Unmanned Vehicle Fusion Position...mentioning

confidence: 99%

RETRACTED ARTICLE: Unmanned Vehicle Fusion Positioning Technology Based on “5G + Beidou” and 3D Point Cloud Image

Fu,

Zhao,

Fan

et al. 2023

Int J Comput Intell Syst

View full text Add to dashboard Cite

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Design of mountain fire prevention monitoring system for transmission lines based on machine vision algorithms

Dong¹,

Ding²,

Ma³

et al. 2023

International Journal of Emerging Electric Power Systems

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With the continuous development of society, power grid has become a major guarantee for social and economic development, and its coverage is also expanding. The power grid not only covers some developed areas, but also extends to remote mountainous areas with complex changes in geological and climatic conditions. However, at the same time, line failures and power outages caused by mountain fires are becoming increasingly frequent, which poses a significant threat to the safety and stable operation of the power system. Therefore, how to solve the problem of mountain fire on transmission lines has become an important topic today. The main purpose of this study is to achieve effective monitoring of transmission line mountain fires and provide more assurance for the stable operation of the power system. This article discussed the transmission line and traditional mountain fire prevention monitoring system, and combined the current hot machine vision algorithm to design the overall mountain fire prevention monitoring system, focusing on analyzing the differences between this system and traditional systems. Through experiments, it was found that the monitoring accuracy of the mountain fire prevention monitoring system designed in this article has been improved by 10.6 % compared to the traditional system at location 4, 7.6 % at location 6, and 7.9 % at location 7. At the same time, the real-time performance and anti-interference performance of this system were also better than traditional systems, and the overall performance of the system was better. Practice has proved that the system can effectively overcome the interference of external environment, and monitor and identify the fire situation in real time and accurately, ensuring the safety of transmission lines. At the same time, the research on machine vision algorithms in this paper can also help promote the more perfect development of power systems. Practice has proved that this system can effectively overcome the interference of the external environment, and continuously and accurately detect and identify the fire situation.

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Image feature extraction algorithm based on visual information

Xu,

Ahmad,

Liao

et al. 2023

Journal of Intelligent Systems

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Vision is the main sensory organ for human beings to contact and understand the objective world. The results of various statistical data show that more than 60% of all ways for human beings to obtain external information are through the visual system. Vision is of great significance for human beings to obtain all kinds of information needed for survival, which is the most important sense of human beings. The rapid growth of computer technology, image processing, pattern recognition, and other disciplines have been widely applied. Traditional image processing algorithms have some limitations when dealing with complex images. To solve these problems, some scholars have proposed various new methods. Most of these methods are based on statistical models or artificial neural networks. Although they meet the requirements of modern computer vision systems for feature extraction algorithms with high accuracy, high speed, and low complexity, these algorithms still have many shortcomings. For example, many researchers have used different methods for feature extraction and segmentation to get better segmentation results. Scale-invariant feature transform (SIFT) is a description used in the field of image processing. This description has scale invariance and can detect key points in the image. It is a local feature descriptor. A sparse coding algorithm is an unsupervised learning method, which is used to find a set of “super complete” basis vectors to represent sample data more efficiently. Therefore, combining SIFT and sparse coding, this article proposed an image feature extraction algorithm based on visual information to extract image features. The results showed that the feature extraction time of X algorithm for different targets was within 0.5 s when the other conditions were the same. The feature matching time was within 1 s, and the correct matching rate was more than 90%. The feature extraction time of Y algorithm for different targets was within 2 s. The feature matching time was within 3 s, and the correct matching rate was between 80 and 90%, indicating that the recognition effect of X algorithm was better than that of Y algorithm. It indicates the positive relationship between visual information and image feature extraction algorithm.

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Improvement and Implementation of Machining and Positioning Method of Intelligent Construction Machinery Components Relying on Machine Vision

Cited by 3 publications

References 8 publications

RETRACTED ARTICLE: Unmanned Vehicle Fusion Positioning Technology Based on “5G + Beidou” and 3D Point Cloud Image

RETRACTED ARTICLE: Unmanned Vehicle Fusion Positioning Technology Based on “5G + Beidou” and 3D Point Cloud Image

Design of mountain fire prevention monitoring system for transmission lines based on machine vision algorithms

Image feature extraction algorithm based on visual information

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