An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

Yu, Xiaolei; Zhou, Yujun; Liu, Zhenlu; Zhao, Zhimin

doi:10.1177/0142331218823864

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…PSO algorithm was proposed by Eberhart and Kennedy (1995), and it is widely used in image processing (Li et al, 2018; Yu et al, 2019a), neural network training (He et al, 2020; Tian et al, 2017), network optimization (Gao et al, 2019; Qi et al, 2019) and other fields. Particle swarm is a set of potential solutions to the problem to be optimized.…”

Section: Main Methodsmentioning

confidence: 99%

Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method

Tan

Liang

Zhang

et al. 2021

Transactions of the Institute of Measurement and Control

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Percutaneous puncture interventional therapy is an important method for pathological examination, local anesthesia, and local drug delivery in modern clinics. Due to the existence of complex obstacles such as nerves, arteries, bones and so on in the puncture path, it is a challenging work to design the optimal path for surgical needle. In this paper, we propose a new path planning method based on the adaptive intelligent particle swarm optimization (PSO) algorithm with parameter adjustment mechanism. First, force and motion analysis are carried out on the bevel-tip flexible needle after piercing into human tissues, the motion model of the needle and the spatial transformation model of puncture route in three-dimensional space are obtained, respectively. Then, a multi-objective function is established, which includes puncture path length function, puncture error function and collision detection function. Finally, the optimal puncture path is obtained based on the adaptive intelligent PSO algorithm. The simulation results show that the newly proposed path planning method has higher efficiency, better adaptability to complex environments and higher accuracy than other path planning methods in literature.

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Section: Main Methodsmentioning

confidence: 99%

Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method

Tan

Liang

Zhang

et al. 2021

Transactions of the Institute of Measurement and Control

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“…Compared with the previous methods [16,31], the deep learning recognition system based on motion blur estimation and improved YOLOv2 has obvious merits. The blur and noise of polluted images are unknown and cannot be completely repaired.…”

Section: Image System Assessmentmentioning

confidence: 99%

Metrology and Measurement Systems

Li,

Yu,

Liu

et al. 2021

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Effective recognition of tags in the dynamic measurement system would significantly improve the reading performance of the tag group, but the blurred outline and appearance of tag images captured in motion seriously limit the effectiveness of the existing tag group recognition. Thus, this paper proposes passive tag group recognition in the dynamic environment based on motion blur estimation and improved YOLOv2. Firstly, blur angles are estimated with a Gabor filter, and blur lengths are estimated through nonlinear modelling of a Generalized Regression Neural Network (GRNN). Secondly, tag recognition based on YOLOv2 improved by a Gaussian algorithm is proposed. The features of the tag group are analyzed by the Gaussian algorithm, the region of interest of the dynamic tag is effectively framed, and the tag foreground is extracted; Secondly, the data set of tag groups are trained by the end-to-end YOLOv2 algorithm for secondary screening and recognition, and finally the specific locations of tags are framed to meet the effective identification of tag groups in different scenes. A considerable number of experiments illustrate that the fusion algorithm can significantly improve recognition accuracy. Combined with the reading distance, the research presented in this paper can more accurately optimize the three-dimensional structure of the tag group, improve the reading performance of the tag group, and avoid the interference and collision of tags in the communication channel. Compared with the previous template matching algorithm, the tag group recognition ability put forward in this paper is improved by at least 13.9%, and its reading performance is improved by at least 6.2% as shown in many experiments.

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“…In the real world, Radio Frequency Identification (RFID) multi-tag dynamic measurement system (RFID multi-tag 3D positioning and laser ranging) is normally affected by various external factors (Yu et al, 2018b, 2019). When multi-tag are captured in Charge Coupled Device (CCD) sensor array of the camera, tag imaging on the focal plane moves in a single exposure time to blur the tag imaging details.…”

Section: Introductionmentioning

confidence: 99%

RFID multi-tag dynamic detection measurement based on conditional generative adversarial nets and YOLOv3

Lin

Liu

et al. 2021

Transactions of the Institute of Measurement and Control

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The quality of multi-tag imaging greatly affects the effective detection of multi-tag. When multi-tag moves rapidly, the image may have serious dynamic blur, and tags can not be detected efficiently. In previous work, it is generally assumed that blur kernel and noise stationary to improve image quality. However, the dynamic deblurring of Radio Frequency Identification (RFID) multi-tag imaging is an ill-posed inverse problem. In this paper, firstly, blur-sharp multi-tag image pairs are made by superimposing and averaging the adjoin random frames. Then, we propose blind deblurring for dynamic RFID multi-tag imaging based on conditional generative adversarial nets (CGANs), which adds perceptual loss and content loss to generator to make image sharper. Finally, tags are detected by YOLOv3 in real time in end-to-end manner. Experimental results demonstrate that PSNR is at least 0.56dB higher and speed is at least 31.25 % faster than that of the current improved convolution neural networks (CNN). CGANs can remove image blur better, which has great superiority in the field of dynamic multi-tag imaging. In addition, YOLOv3 detects multi-tag quickly, thereby improving the detection accuracy.

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An optimal measurement method for spatial distribution of radio frequency identification multi-tag based on image analysis and PSO

Cited by 5 publications

References 23 publications

Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method

Path planning of surgical needle: A new adaptive intelligent particle swarm optimization method

Metrology and Measurement Systems

RFID multi-tag dynamic detection measurement based on conditional generative adversarial nets and YOLOv3

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