Locator slope calculation via deep representations based on monocular vision

Yang, Yang; Zhang, Wensheng; He, Zewen; Chen, Dongjie

doi:10.1007/s00521-017-3229-8

Cited by 3 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To guarantee the current collection quality and to allow vehicles to run smoothly and safely, it is vital to monitor the stability of the catenary cantilever device structure periodically [2], [3]. With the rapid development of artificial intelligence technologies, advanced vision-based noncontact detection methods for component failures [4]- [10] and structure parameters measurement [11]- [14] of the catenary are proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…They used a binocular vision component composed of two line-scan cameras to calculate the space intersection point, and then the parameters were obtained according to the triangulation measurement principle. Yang et al [14] proposed a parameter detection method for the steady arm slope of catenary cantilever devices. First, the steady arm was located and extracted by combining the CNN-based rough detection and the Hough transformation-based fine detection.…”

Section: Introductionmentioning

confidence: 99%

“…The 2-D images-based methods discussed mainly focus on the parameter detection for simple environments, such as the geometric parameter of catenary suspension devices and the steady arm slope parameter of catenary cantilever devices. The detection of these parameters is not susceptible to background interference and is, in general, evaluated under simple conditions, especially the catenary suspension devices [11]- [14]. However, for the detection of the SPCCDs, as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

High-Precision Detection Method for Structure Parameters of Catenary Cantilever Devices Using 3-D Point Cloud Data

Liu

et al. 2021

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

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 of the proposed detection method are: 1) segmenting and recognizing the components of the catenary cantilever devices, 2) extracting the detection plane and backbone component axis of catenary cantilever devices, and 3) detecting the SPCCD. The effective segmentation of components is critical for structure parameter detection. A point cloud segmentation and recognition method based on three-dimensional convolutional neural networks (3-D CNNs) is introduced to determine the different components of the catenary cantilever devices. Compared with traditional unsupervised clustering procedures for point cloud segmentation, the proposed method can improve the segmentation accuracy, does not require complex tuning procedures of parameters, and improves robustness and stability. Additionally, the segmentation method defines a recognition function, facilitating the analysis of the structural relationship between objects. Furthermore, we proposed an improved projection random sample consensus (RANSAC) method, which can effectively divide the detection plane of catenary cantilever devices to solve the multicantilever device occlusion problem. With RANSAC, it is also possible to precisely extract the backbone component axis and enhance parameter detection accuracy. The experimental results show that the structure angle and steady arm slope's error accuracy can achieve 0.1029 • and 1.19%, respectively, which indicates the proposed approach can precisely detect the SPCCD.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High-Precision Detection Method for Structure Parameters of Catenary Cantilever Devices Using 3-D Point Cloud Data

Liu

et al. 2021

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

show abstract

“…Because the DL performs well on high-dimensional data, it is being used in many domains, including image recognition [14][15][16][17], speech recognition [18][19][20], and natural language processing [21,22]. It is also used in network information security [23,24], industrial data processing [25], and drug molecular research [26]. The main flow chart of ML applied to OMI image processing is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Application of machine learning method in optical molecular imaging: a review

Meng

Gao

et al. 2019

Sci. China Inf. Sci.

View full text Add to dashboard Cite

Optical molecular imaging (OMI) is an imaging technology that uses an optical signal, such as near-infrared light, to detect biological tissue in organisms. Because of its specific and sensitive imaging performance, it is applied in both preclinical research and clinical surgery. However, it requires heavy data analysis and a complex mathematical model of tomographic imaging. In recent years, machine learning (ML)-based artificial intelligence has been used in different fields because of its ability to perform powerful data processing. Its analytical capability for processing complex and large data provides a feasible scheme for the requirement of OMI. In this paper, we review ML-based methods applied in different OMI modalities.

show abstract