Automatic recognition for catenary insulators of high-speed railway based on contourlet transform and Chan–Vese model

Zhang, Guinan; Liu, Zhigang; Han, Ye

doi:10.1016/j.ijleo.2015.10.049

Cited by 21 publications

(3 citation statements)

References 30 publications

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“…The method used a segment clustering algorithm to divide the images and detected the rod-insulators using deformable part models. Zhang et al [8] used the contourlet transform to extract insulator feature information based on the anisotropy and directionality of catenary rod-insulators (component number 2 in Figure 1). The Chan-Vese model was used to detect the insulator boundaries to locate each insulator.…”

Section: Introductionmentioning

confidence: 99%

Unified Deep Learning Architecture for the Detection of All Catenary Support Components

Liu

Núñez

et al. 2020

IEEE Access

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With the rapid development of deep learning technologies, researchers have begun to utilize convolutional neural network (CNN)-based object detection methods to detect multiple catenary support components (CSCs). The literature has focused on the detection of specified large-scale CSCs. Additionally, CNN architectures have faced difficulties in identifying overlapping CSCs, especially small-scale components. In this paper, a unified CNN architecture is proposed for detecting all components at various scales of CSCs. First, a detection network for CSCs with large scales is proposed by optimizing and improving Faster R-CNN. Next, a cascade network for the detection of CSCs with small scales is proposed and is integrated into the detection network for CSCs with large scales to construct the unified network architecture. The experimental results demonstrate that the detection accuracy of the proposed CNN architecture can reach 92.8%; hence, it outperforms the popular CNN architectures. INDEX TERMS High-speed railway catenary, catenary support component detection, deep learning architecture.

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Section: Introductionmentioning

confidence: 99%

Unified Deep Learning Architecture for the Detection of All Catenary Support Components

Liu

Núñez

et al. 2020

IEEE Access

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“…A semi-local region descriptor is used in an active contour model to overcome the difficulties caused by the texture inhomogeneity [19]. Reference [20] uses the contourlet transformation for insulator texture analysis and then the fuzzy c-means is applied to cluster the insulator texture feature points to locate the initial curve. Chan-Vese model is finally used to detect the insulator boundaries.…”

Section: Introductionmentioning

confidence: 99%

Texture-and-Shape Based Active Contour Model for Insulator Segmentation

Cao

Wang

et al. 2019

IEEE Access

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Insulator segmentation is a critical step for automatic insulator fault diagnosis in high voltage transmission systems. Existing methods fail to segment insulators when they have a low contrast with the surroundings. Considering the unique shape and texture characteristics of insulators, a texture-and-shape based active contour model is proposed for insulator segmentation. The segmentation is achieved by evolving a curve iteratively by the texture features and shape priors. In the texture-driven curve evolution, a semi-local region descriptor is used to extract the texture features of insulators and a new convex energy functional is defined based on the extracted features with the topology-preserving term. The topology-preserving term keeps the curve's topology unchanged as the curve topology is determined by the shape template. In the shape-driven curve evolution, the shape context descriptor is used to align the shape template with the current curve. The semantic transformation between the shape template and the current curve is obtained by Procrustes analysis and then adopted to update the current curve to resemble the shape prior. The proposed method is applied to a set of images, and the experimental results confirm the efficacy and effectiveness of the proposed method for segmenting insulators in cluttered backgrounds.

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“…In [17], the insulators were segmented by the distribution of Harris corners, which was only suitable for images with complete insulators and a concise background. Some special segmentation methods utilised the mounting structure information to produce an accurate positioning [18, 19].…”

Section: Introductionmentioning

confidence: 99%

Real‐time segmentation of various insulators using generative adversarial networks

Chang

Yang

et al. 2018

IET Computer Vision

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The conventional inspection of fragile insulators is critical to grid operation and insulator segmentation is the basis of inspection. However, the segmentation of various insulators is still difficult because of the great differences in colour and shape, as well as the cluttered background. Traditional insulator segmentation algorithms need many artificial thresholds, thereby limiting the adaptability of algorithms. A compact end-to-end neural network, which is trained in the framework of conditional generative adversarial networks, is proposed for the real-time pixel-level segmentation of insulators. The input image is mapped to a visual saliency map, and various insulators with different poses are filtered out at the same time. The proposed two-stage training and empty samples are also used to improve the segmentation quality. Extensive experiments and comparisons are performed on many real-world images. The experimental results demonstrate superior segmentation and real-time performance. Meanwhile, the effectiveness of the proposed training strategies and the trade-off between performance and speed are analysed in detail.

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Automatic recognition for catenary insulators of high-speed railway based on contourlet transform and Chan–Vese model

Cited by 21 publications

References 30 publications

Unified Deep Learning Architecture for the Detection of All Catenary Support Components

Unified Deep Learning Architecture for the Detection of All Catenary Support Components

Texture-and-Shape Based Active Contour Model for Insulator Segmentation

Real‐time segmentation of various insulators using generative adversarial networks

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