Buckle Pose Estimation Using a Generative Adversarial Network
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Published:2023-03-27
Issue:7
Volume:13
Page:4220
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Feng Hanfeng1,
Chen Xiyu2,
Zhuang Jiayan2,
Song Kangkang2,
Xiao Jiangjian2,
Ye Sichao2
Affiliation:
1. College of Electrical Engineering Computer Science, Ningbo University, Ningbo 315211, China
2. Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China
Abstract
The buckle before the lens coating is still typically disassembled manually. The difference between the buckle and the background is small, while that between the buckles is large. This mechanical disassembly can also damage the lens. Therefore, it is important to estimate pose with high accuracy. This paper proposes a buckle pose estimation method based on a generative adversarial network. An edge extraction model is designed based on a segmentation network as the generator. Spatial attention is added to the discriminator to help it better distinguish between generated and real graphs. The generator thus generates delicate external contours and center edge lines with help from the discriminator. The external rectangle and the least square methods are used to determine the center position and deflection angle of the buckle, respectively. The center point and angle accuracies of the test datasets are 99.5% and 99.3%, respectively. The pixel error of the center point distance and the absolute error of the angle to the horizontal line are within 7.36 pixels and 1.98°, respectively. This method achieves the highest center point and angle accuracies compared to Hed, RCF, DexiNed, and PidiNet. It can meet practical requirements and boost the production efficiency of lens coatings.
Funder
Zhejiang Provincial Natural Science Foundation
Technology Innovation 2025 Major Project
Ningbo Medical Science and Technology Plan Project
Ningbo Science and Technology Program for the Public Interest
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
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