Intelligent Bamboo Part Sorting System Design via Machine Vision

Liu, Tianhu; Wu, Zi-Di; Chen, Qin-Ling; Nie, Xiang-Ning; Li, Guiqi; Wang, Hongjun; Zhang, Di; Liu, Wei; Wu, Jinmeng

doi:10.13073/fpj-d-20-00030

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Firstly, a camera was used to acquire the image of the item to be sorted, then RBF neural network algorithm was used to acquire the defective features in the image and localize the features, and finally an air jet was designed to extrude the defective items from the conveyor belt. The performance of the whole system was tested and it was found that the system has a high removal rate of 91.7% for defective items 25 . To facilitate port automation, Miao et al proposed a 3D point cloud hull modeling and operational target recognition algorithm based on a laser measurement collection system.…”

Section: Discussionmentioning

confidence: 99%

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

Zhang,

Zhu,

Chen

et al. 2024

Sci Rep

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With the rapid development of modern science and technology, navigation technology provides great convenience for people's life, but the problem of inaccurate localization in complex environments has always been a challenge that navigation technology needs to be solved urgently. To address this challenge, this paper proposes an augmented reality navigation method that combines image segmentation and multi-sensor fusion tracking registration. The method optimizes the image processing process through the GA-OTSU-Canny algorithm and combines high-precision multi-sensor information in order to achieve accurate tracking of positioning and guidance in complex environments. Experimental results show that the GA-OTSU-Canny algorithm has a faster image edge segmentation rate, and the fastest start speed is only 1.8 s, and the fastest intersection selection time is 1.2 s. The navigation system combining the image segmentation and sensor tracking and registration techniques has a highly efficient performance in real-world navigation, and its building recognition rates are all above 99%. The augmented reality navigation system not only improves the navigation accuracy in high-rise and urban canyon environments, but also significantly outperforms traditional navigation solutions in terms of navigation startup time and target building recognition accuracy. In summary, this research not only provides a new framework for the theoretical integration of image processing and multi-sensor data, but also brings innovative technical solutions for the development and application of practical navigation systems.

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Section: Discussionmentioning

confidence: 99%

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

Zhang,

Zhu,

Chen

et al. 2024

Sci Rep

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show abstract

“…The performance of the system was examined and found to have a classification time of only 0.002 s for faulty images. Liu et al 31 developed an intelligent sorting system based on machine vision using neural network algorithms. First, a camera was used to acquire the image of the item to be sorted, then RBF neural network algorithm was used to acquire the defective features in the image and localize the features, and finally an air jet was designed to extrude the defective items from the conveyor belt.…”

Section: Discussionmentioning

confidence: 99%

Autonomous perception method of multi‐degree‐of‐freedom industrial robot arm trajectory

Qian

2023

Adv Control Appl

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In this study, a novel autonomous sensing method of multi‐degree‐of‐freedom industrial robot arm trajectory is proposed. The research takes the distance sensor to collect environmental data, and takes the point cloud data scanned by 3D laser as the basis. The environment model of multi‐degree‐of‐freedom industrial robotic arm is established by Iterative Closest Point (ICP). Then the target object is calibrated by binocular imaging technology. Subsequently, angle of each joint of multi‐degree‐of‐freedom industrial robotic arm is calculated to determine the spatial attitude of the robotic arm. In addition, 3D LiDAR is installed at the end of the robotic arm, and the end trajectory of multi‐degree‐of‐freedom industrial robotic arm is sensed autonomously by using the optimal function. The proposed method has advantages of high accuracy and short sensing time in autonomous sensing of multi‐degree‐of‐freedom industrial robot arm trajectory.

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“…Based on machine vision technology, Wang et al (2021c) carried out extreme learning machine ELM to classify the color of solid wood floor, and used three different algorithms, namely grey wolf optimization (GWO), genetic optimization (GA), and particle swarm optimization (PSO), to optimize and compare their recognition efficiency and accuracy, providing an effective solution for the online intelligent sorting of solid wood floor color in household enterprises. Liu et al (2018) applied the machine vision to the automatic color sorting system of bamboo slice. Through the machine vision to analyze, process and extract the color characteristics of bamboo slices, and combined with the pattern recognition algorithm to design the sorting system, the rapid sorting of bamboo slices was finally achieved.…”

Section: Surface Color Analysis Of Wood Productsmentioning

confidence: 99%

The applications of machine vision in raw material and production of wood products

Wang

Zhan²,

et al. 2022

BioRes

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Machine vision has been developed nearly for 70 years and been widely applied in electronics, automotive manufacturing, food processing, etc. With deepening study of its theory and technology in forestry industry, the industry of wood products is moving steadily toward the goal of automated identification and production to improve the manufacturing intelligence of enterprises. In this study, theoretical and algorithmic research on image acquisition, feature extraction, recognition, and classification involved in machine vision-based wood recognition technology were analyzed on the basis of its global development. The applications of machine vision in the wood materials, such as the identification of tree species, wood inspection and classification, defects detection of wood product, surface analysis of wood color, and quality control of furnishing products were thoroughly analyzed. The development trend of machine vision in the production and management of wood materials was considered in the current development of wood and furnishing enterprises. These results lay a solid foundation for wood science research, and intelligent manufacture of wooden furniture, and efficient development of greener and cleaner production of the furniture industry, which could improve the environmental effect of the wood products and furniture and make a great contribution for the carbon goal of “30-60” in China.

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Intelligent Bamboo Part Sorting System Design via Machine Vision

Cited by 3 publications

References 15 publications

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

Augmented reality navigation method based on image segmentation and sensor tracking registration technology

Autonomous perception method of multi‐degree‐of‐freedom industrial robot arm trajectory

The applications of machine vision in raw material and production of wood products

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