10 results on '"Tang, Yunchao"'
Search Results
2. Real-time detection of asymmetric surface deformation and field stress in concrete-filled circular steel tubes via multi-vision method
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Tang, Yunchao, Huang, Kuangyu, Li, Lijuan, Zou, Xiangjun, Feng, Wenxian, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Ha-Minh, Cuong, editor, Dao, Dong Van, editor, Benboudjema, Farid, editor, Derrible, Sybil, editor, Huynh, Dat Vu Khoa, editor, and Tang, Anh Minh, editor
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- 2020
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3. Real-Time Defect Detection for Metal Components: A Fusion of Enhanced Canny–Devernay and YOLOv6 Algorithms.
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Wang, Hongjun, Xu, Xiujin, Liu, Yuping, Lu, Deda, Liang, Bingqiang, and Tang, Yunchao
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METAL detectors ,METAL defects ,DEEP learning ,COMPUTER vision ,SURFACE defects ,METALS in the body - Abstract
Due to the presence of numerous surface defects, the inadequate contrast between defective and non-defective regions, and the resemblance between noise and subtle defects, edge detection poses a significant challenge in dimensional error detection, leading to increased dimensional measurement inaccuracies. These issues serve as major bottlenecks in the domain of automatic detection of high-precision metal parts. To address these challenges, this research proposes a combined approach involving the utilization of the YOLOv6 deep learning network in conjunction with metal lock body parts for the rapid and accurate detection of surface flaws in metal workpieces. Additionally, an enhanced Canny–Devernay sub-pixel edge detection algorithm is employed to determine the size of the lock core bead hole. The methodology is as follows: The data set for surface defect detection is acquired using the labeling software lableImg and subsequently utilized for training the YOLOv6 model to obtain the model weights. For size measurement, the region of interest (ROI) corresponding to the lock cylinder bead hole is first extracted. Subsequently, Gaussian filtering is applied to the ROI, followed by a sub-pixel edge detection using the improved Canny–Devernay algorithm. Finally, the edges are fitted using the least squares method to determine the radius of the fitted circle. The measured value is obtained through size conversion. Experimental detection involves employing the YOLOv6 method to identify surface defects in the lock body workpiece, resulting in an achieved mean Average Precision ( m A P ) value of 0.911. Furthermore, the size of the lock core bead hole is measured using an upgraded technique based on the Canny–Devernay sub-pixel edge detection, yielding an average inaccuracy of less than 0.03 mm. The findings of this research showcase the successful development of a practical method for applying machine vision in the realm of the automatic detection of metal parts. This achievement is accomplished through the exploration of identification methods and size-measuring techniques for common defects found in metal parts. Consequently, the study establishes a valuable framework for effectively utilizing machine vision in the field of metal parts inspection and defect detection. [ABSTRACT FROM AUTHOR]
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- 2023
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4. A Study on Long-Close Distance Coordination Control Strategy for Litchi Picking.
- Author
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Wang, Hongjun, Lin, Yiyan, Xu, Xiujin, Chen, Zhaoyi, Wu, Zihao, and Tang, Yunchao
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LITCHI ,POINT cloud ,COMPUTER vision ,ROBOTICS ,CAMERAS - Abstract
For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long-close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of "Point + Line" was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots. [ABSTRACT FROM AUTHOR]
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- 2022
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5. Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.
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Tang, Yunchao, Chen, Mingyou, Wang, Chenglin, Luo, Lufeng, Li, Jinhui, Lian, Guoping, and Zou, Xiangjun
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COMPUTER vision ,ROBOT vision ,DIGITAL image processing ,AGRICULTURAL robots ,VISION ,FRUIT - Abstract
The utilization of machine vision and its associated algorithms improves the efficiency, functionality, intelligence, and remote interactivity of harvesting robots in complex agricultural environments. Machine vision and its associated emerging technology promise huge potential in advanced agricultural applications. However, machine vision and its precise positioning still have many technical difficulties, making it difficult for most harvesting robots to achieve true commercial applications. This article reports the application and research progress of harvesting robots and vision technology in fruit picking. The potential applications of vision and quantitative methods of localization, target recognition, 3D reconstruction, and fault tolerance of complex agricultural environment are focused, and fault-tolerant technology designed for utilization with machine vision and robotic systems are also explored. The two main methods used in fruit recognition and localization are reviewed, including digital image processing technology and deep learning-based algorithms. The future challenges brought about by recognition and localization success rates are identified: target recognition in the presence of illumination changes and occlusion environments; target tracking in dynamic interference-laden environments, 3D target reconstruction, and fault tolerance of the vision system for agricultural robots. In the end, several open research problems specific to recognition and localization applications for fruit harvesting robots are mentioned, and the latest development and future development trends of machine vision are described. [ABSTRACT FROM AUTHOR]
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- 2020
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6. Three-dimensional Date Reconstruction and Navigation of Complex Scene in Virtual Environment
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Tang Yunchao, Zou Xiang-Jun, Chen Yan, Sun Shuang, Meng Qing-Guo, and Luo Lufeng
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Image fusion ,Carving ,business.industry ,Computer science ,3D reconstruction ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Navigation system ,Solid modeling ,Iterative reconstruction ,Virtual reality ,Data modeling ,Image texture ,Computer graphics (images) ,Computer vision ,Artificial intelligence ,Geometric modeling ,business ,Texture mapping ,ComputingMethodologies_COMPUTERGRAPHICS - Abstract
This Targeting to the complexity and large amount of 3D(three-dimensional) reconstruction of complex scene in virtual environment, Firstly, The 3D data acquisition and conversion principle, scene modeling and texture mapping method were described. Secondly, the geometric modeling and image fusion method was presented. Thirdly, the reconstruction mathematical model of image based on entropy variation were introduced, and then data compression algorithm to reduce the number of the entity grid was given. Besides, the intricate and fine animal wood carving model on the roof was an example, which was optimized and reconstructed. At last, a classical Chinese architecture was used as a prototype for the fusion modeling and optimization, and merging with virtual software a 3D reconstruction of classical architecture and the scene Navigation system in virtual environment was developed with VC++.
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- 2010
7. Novel visual crack width measurement based on backbone double-scale features for improved detection automation.
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Tang, Yunchao, Huang, Zhaofeng, Chen, Zheng, Chen, Mingyou, Zhou, Hao, Zhang, Hexin, and Sun, Junbo
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WIDTH measurement , *ERROR rates , *AUTOMATION , *SPINE , *COMPUTER vision , *CRACKING of concrete - Abstract
• Backbone neighborhood distribution points are reduced to facilitate classification. • Dual-scale backbone features are combined for accurate width measurement direction. • A detailed visual measurement process of crack width is proposed. • A visual measurement method of crack width that is closer to reality is used to obtain more accurate results. State-of-the-art machine-vision systems have limitations associated with crack width measurements. The sample points used to describe the crack width are often subjectively defined by experimenters, which obscures the crack width ground truth. Consequently, in most related studies, the uncontrollable system errors of vision modules result in unsatisfactory measurement accuracy. In this study, the cracks of a reservoir dam are taken as objects, and a new crack backbone refinement algorithm and width-measurement scheme are proposed. The algorithm simplifies the redundant data in the crack image and improves the efficiency of crack-shape estimation. Further, an effective definition of crack width is proposed that combines the macroscale and microscale characteristics of the backbone to obtain accurate and objective sample points for width description. Compared with classic methods, the average simplification rate of the crack backbone and the average error rate of direction determination are all improved. The results of a series of experiments validate the efficacy of the proposed method by showing that it can improve detection automation and has potential engineering application. [ABSTRACT FROM AUTHOR]
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- 2023
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8. Dynamic visual servo control methods for continuous operation of a fruit harvesting robot working throughout an orchard.
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Chen, Mingyou, Chen, Zengxing, Luo, Lufeng, Tang, Yunchao, Cheng, Jiabing, Wei, Huiling, and Wang, Jinhai
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FRUIT harvesting , *ROBOTS , *FIELD research , *ORCHARDS , *COMPUTER vision , *HARVESTING - Abstract
• Picking robots must operate continuously throughout the orchard for commercial and practical purposes. • Efficient visual servo control methods of locomotion and picking are necessary for full-field harvesting. • Serialized keyframes from SLAM provide high-confidence global crop information. • The multi-map fusion mechanism greatly improves locomotion stability, despite computational costs. • Solely relying on multi-view positioning does not enhance picking accuracy but affects picking efficiency. Fruit-picking robots are crucial for achieving efficient orchard harvesting. To genuinely meet the commercial production needs of farmers, the new generation of fruit-picking robots must be capable of demonstrating complete and continuous observation, movement, and picking behaviors throughout complex orchards, akin to real human employees. This poses systematic challenges, as many prior researches have focused solely on a part of the continuous operation of the entire orchard, such as fruit positioning, navigation, path planning, or grasping. These isolated basic functions are important but insufficient for fulfilling operational requirements on a macro scale and continuous situation. Developing an efficient control method for each basic module and constructing their internal coordination is vital for transitioning a harvesting robot from a functional prototype to a practical machine. In this context, this study tackles the visual servo control problem for efficient locomotion, picking, and their seamless integration. A set of vision algorithms for locomotion destination estimation, real-time self-positioning, and dynamic harvesting is proposed. Additionally, a solid coordination mechanism for continuous locomotion and picking behavior is established. Each method offers distinct advantages, such as improved accuracy, adaptability to varying conditions, and enhanced picking efficiency, enabling the robot to operate autonomously and continuously. Comprehensive field experiments validated the soundness of the methods. The primary contribution of this study lies in addressing the challenge of continuous operation in an entire orchard as a systematic problem and providing new insights into control methods for the future development of highly autonomous, practical, and user-oriented fruit harvesting systems. [ABSTRACT FROM AUTHOR]
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- 2024
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9. 3D vision technologies for a self-developed structural external crack damage recognition robot.
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Hu, Kewei, Chen, Zheng, Kang, Hanwen, and Tang, Yunchao
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STRUCTURAL health monitoring , *OPTICAL radar , *LIDAR , *BINOCULAR vision , *DEPTH perception , *IMAGE registration - Abstract
Persistent cracking and progressive damage can weaken the operational performance of structures such as bridges, dams, and concrete buildings. Consequently, research into automated, high-precision crack detection methods remains pivotal within the realm of structural health monitoring (SHM). Presently, scholars predominantly rely on two-dimensional (2D) image-based algorithms for crack detection. However, these methods commonly struggle to accurately locate the three-dimensional (3D) coordinates of cracks on large structures and to extract the 3D contours of cracks. To address this challenge, this study proposes an automated 3D crack detection system for structures based on high-precision Light Detection and Ranging (LiDAR) and camera fusion. Firstly, precise registration of images and LiDAR point clouds was achieved through accurate extrinsic calibration of the sensors. Secondly, the lightweight MobileNetV2_DeepLabV3 crack semantic segmentation network was employed to detect and locate cracks. Finally, by automatically guiding the robotic arm, an industry-standard depth camera was able to capture high-precision 3D information about the crack at close observation points. Compared with the existing studies, this study emphasizes the extraction of high-precision 3D crack features and verifies the validity of the method by comparing the measurement results with those of the traditional method, demonstrating a remarkable measurement accuracy reaching sub-millimeter levels (0.1 mm). Moreover, the study introduces a comprehensive hardware platform and algorithmic framework, offering pioneering theoretical methodologies and replicable equipment references for automated surveillance and monitoring systems dedicated to structural health. • An automated 3D crack detection system for structures, achieving sub-millimeter accuracy by fusing LiDAR and depth camera. • Overcomes the limitations of 2D methods, providing high-precision spatial positioning and crack measurement. • LiDAR-Camera fusion enhances depth perception beyond single-source sensors. [ABSTRACT FROM AUTHOR]
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- 2024
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10. Transforming unmanned pineapple picking with spatio-temporal convolutional neural networks.
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Meng, Fan, Li, Jinhui, Zhang, Yunqi, Qi, Shaojun, and Tang, Yunchao
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PINEAPPLE , *CONVOLUTIONAL neural networks , *TRANSFORMER models , *COMPUTER vision , *AGRICULTURE , *AGRICULTURAL development - Abstract
Automated pineapple harvesting has emerged as a prominent prospective development within the agricultural domain. Nevertheless, the intricate growth conditions that pineapples encounter in the field, such as inadequate light, overexposure, obstructions caused by fruit leaves, or the overlapping of fruits, pose substantial challenges to the accuracy and robustness of traditional real-time detection algorithms. In recent times, Transformer models, when applied to computer vision, have exhibited commendable performance, underscoring their potential for target detection in smart agricultural applications. In this report, we propose a spatio-temporal convolutional neural network model that leverages the shifted window Transformer fusion region convolutional neural network model for the purpose of detecting pineapple fruits. Our study includes a comparative analysis of these results and those obtained through the utilization of conventional models. Additionally, we investigate the influence of various aspects of data preparation, including image resolution, object size, and object complexity, on the ultimate pineapple detection outcomes. Experimental findings elucidate that, in the case of detecting a single-category target like a pineapple, the employment of 2000 annotated supervised data points yields the optimal detection accuracy. Further augmenting the size of the training dataset does not yield any significant improvement in detection accuracy. Furthermore, images of pineapples captured from greater distances encompass smaller targets and an increased number of pineapple instances, rendering them more intricate and challenging to accurately detect. In summary, our study employs the spatio-temporal convolutional neural network model to attain pineapple detection with an impressive accuracy rate of 92.54% and an average inference time of 0.163 s, thus affirming the efficacy of our developed model in achieving superior detection results. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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