High-Precision Detection Method for Structure Parameters of Catenary Cantilever Devices Using 3-D Point Cloud Data
نویسندگان
چکیده
This article proposes an automatic high-precision detection method for structure parameters of catenary cantilever devices (SPCCDs) using 3-D point cloud data. The steps the proposed are: 1) segmenting and recognizing components devices, 2) extracting plane backbone component axis 3) detecting SPCCD. effective segmentation is critical parameter detection. A recognition based on three-dimensional convolutional neural networks (3-D CNNs) introduced to determine different devices. Compared with traditional unsupervised clustering procedures segmentation, can improve accuracy, does not require complex tuning parameters, improves robustness stability. Additionally, defines a function, facilitating analysis structural relationship between objects. Furthermore, we improved projection random sample consensus (RANSAC) method, which effectively divide solve multicantilever device occlusion problem. With RANSAC, it also possible precisely extract enhance accuracy. experimental results show that angle steady arm slope's error accuracy achieve 0.1029° 1.19%, respectively, indicates approach detect
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ژورنال
عنوان ژورنال: IEEE Transactions on Instrumentation and Measurement
سال: 2021
ISSN: ['1557-9662', '0018-9456']
DOI: https://doi.org/10.1109/tim.2020.3045801