An algorithm for extracting spray trajectory based on laser vision

Wu, Jinghua; Zhu, Keyu

doi:10.1109/icct.2017.8359899

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…Wu et al [14] used a camera and a line laser to reconstruct the 3D data of the sole and could not realize the one-time 3D reconstruction with low working efficiency. Zhu et al [15] proposed an automatic generation method for shoe sole spraying trajectory, which converts the 3D point cloud of shoe soles into 2D depth images, so the accuracy and robustness of this method were not high. Both Caruso et al [16] and Pagano et al [17] used the Kinect V2 sensor as a 3D measurement device to measure the information of soles.…”

Section: Introductionmentioning

confidence: 99%

Method of generating roughening paths for shoe soles based on monocular encoded structured light

et al. 2023

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In this paper, we propose a monocular encoded structured light-based path generation method for sole roughening, and the obtained paths are used to guide a six-degree-of-freedom (DOF) robot for automatic roughening, which greatly improves the quality and efficiency of roughening. First, we selected monocular structured light as the vision system. To improve the calibration and reconstruction accuracy of the projector, we used an improved Three-Wavelength Phase-Shift Profilometry technique (TWPSP) to calibrate the projector, and the accuracy was improved by 26.29% compared with the conventional method. Then, we proposed a Three Coordinate Scanning Weighted-Principal Component Analysis (TCSW-PCA) algorithm as the path planning system. The experimental results show that the six-DOF-robot can automatically roughen different types of soles according to the generated roughening paths, and the processing efficiency and finished product quality of our proposed method of automatic roughening are better than other roughening methods, with an average improvement of 42% in roughening effect and an average improvement of 30.52% in roughness.

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Section: Introductionmentioning

confidence: 99%

Method of generating roughening paths for shoe soles based on monocular encoded structured light

et al. 2023

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“…However, most of the existing methods for generating spray trajectories during automated shoe sole spraying suffer from several significant drawbacks and limitations [3,4]. For example, J. Wu et al put forward a block maximum algorithm to accurately obtain the sole profile.…”

Section: Introductionmentioning

confidence: 99%

“…For example, J. Wu et al put forward a block maximum algorithm to accurately obtain the sole profile. In their groundbreaking research, they constructed an algorithm to precisely capture the intricate details of the shoe sole [3]. G. Xu et al demonstrated an automatic interpolation algorithm for the NURBS (Non-Uniform Rational B-Splines) trajectory of shoe sole spraying based on a 7-DOF (degrees of freedom) robot.…”

Section: Introductionmentioning

confidence: 99%

Deconvolutional Neural Network for Generating Spray Trajectory of Shoe Soles

Wang

et al. 2023

Electronics

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The footwear industry is moving towards automation and intellectualization. To overcome the drawbacks of the high-cost and low-efficiency traditional manual shoe sole gluing process, automatic methods were utilized for generating spray trajectories. Currently, most of the reported automatic methods for generating spray trajectories mainly rely on the outer contour bias method. However, the glue is only applied to the area near the edge/contour of shoe soles and the fixed offset distance in the outer contour bias method cannot adapt to the immense amount of shoe styles with high precision and achieve applicability for irregular and unique sole designs. An intuitive yet logical approach to fulfill the requirements is to utilize the deconvolutional neural network for generating shoe sole spray trajectories. In this work, we treated the glue trajectory prediction as an image-to-image prediction and established a novel deconvolutional neural network to generate shoe sole spray trajectories. The as-proposed deconvolutional neural network for generating spray trajectory offered significant advantages over the existing bias-based methods, including: (1) based on the novel deconvolutional neural network, the proposed method for generating shoe sole spray trajectories exhibits greater applicability to irregular shoe soles, which improves the spray accuracy without compromising the spray efficiency; (2) we discard all the pooling layers, which only consist of convolutional and deconvolutional layers, to preserve more spatial information and achieve higher spray accuracy through end-to-end mapping from shoe sole images to shoe sole spray trajectories, resulting in an improved spray accuracy without sacrificing spray efficiency. The Dice similarity coefficient and Hausdorff distance were used as the evaluation metrics to assess the performance of our approach. Our proposed method showed an ultra-high accuracy and precision with a Dice similarity coefficient over 99.25% and a Hausdorff distance less than 1.2 mm, which are ~10% higher than the spray accuracy of other reported traditional methods. Our findings would bring significant improvements to the field of automatic shoe sole spray trajectory generation, which has the potential to promote the utilization of intelligent technologies in the footwear industry.

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3D Modeling for Upper of Shoe Based on Bilateral Triangulation

Lian

Huang

2018

Recent Advances in Intelligent Information Hiding and Multimedia Signal Processing

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An algorithm for extracting spray trajectory based on laser vision

Cited by 3 publications

References 6 publications

Method of generating roughening paths for shoe soles based on monocular encoded structured light

Method of generating roughening paths for shoe soles based on monocular encoded structured light

Deconvolutional Neural Network for Generating Spray Trajectory of Shoe Soles

3D Modeling for Upper of Shoe Based on Bilateral Triangulation

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