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Improved Lightweight Deep Learning Algorithm in 3D Reconstruction

Tao Zhang1,*, Yi Cao2

1 School of Mechanical Engineering, North China University of Water Conservancy and Hydroelectric Power, Zhengzhou, 450045, China
2 Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, N9B 3P4, Canada

* Corresponding Author: Tao Zhang. Email: email

Computers, Materials & Continua 2022, 72(3), 5315-5325.


The three-dimensional (3D) reconstruction technology based on structured light has been widely used in the field of industrial measurement due to its many advantages. Aiming at the problems of high mismatch rate and poor real-time performance caused by factors such as system jitter and noise, a lightweight stripe image feature extraction algorithm based on You Only Look Once v4 (YOLOv4) network is proposed. First, Mobilenetv3 is used as the backbone network to effectively extract features, and then the Mish activation function and Complete Intersection over Union (CIoU) loss function are used to calculate the improved target frame regression loss, which effectively improves the accuracy and real-time performance of feature detection. Simulation experiment results show that the model size after the improved algorithm is only 52 MB, the mean average accuracy (mAP) of fringe image data reconstruction reaches 82.11%, and the 3D point cloud restoration rate reaches 90.1%. Compared with the existing model, it has obvious advantages and can satisfy the accuracy and real-time requirements of reconstruction tasks in resource-constrained equipment.


Cite This Article

APA Style
Zhang, T., Cao, Y. (2022). Improved lightweight deep learning algorithm in 3D reconstruction. Computers, Materials & Continua, 72(3), 5315-5325.
Vancouver Style
Zhang T, Cao Y. Improved lightweight deep learning algorithm in 3D reconstruction. Comput Mater Contin. 2022;72(3):5315-5325
IEEE Style
T. Zhang and Y. Cao, "Improved Lightweight Deep Learning Algorithm in 3D Reconstruction," Comput. Mater. Contin., vol. 72, no. 3, pp. 5315-5325. 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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