Catenary insulator defect detection based on contour features and gray similarity matching

Tan, Ping; Li, Xufeng; Xu, Jin-mei; Ma, Jien; Wang, Feijie; Ding, Jingzhong; Fang, Youtong; Ning, Yong

doi:10.1631/jzus.a1900341

Cited by 39 publications

(21 citation statements)

References 19 publications

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“…Different DL-based solutions perform defect inspection on CSD components. In [134], the authors applied a DL-based approach to detect the insulators before performing the defect detection through Contour Features and Gray similarity matching. Reference [114] uses both a classical approach (based on SURF) and an AlexNet-based method to detect insulators and bolts in images of CSD in metro power supply systems.…”

Section: A Catenary Support Device (Csd)mentioning

confidence: 99%

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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Within Artificial Intelligence, Deep Learning (DL) represents a paradigm that has been showing unprecedented performance in image and audio processing by supporting or even replacing humans in defect and anomaly detection. The Railway sector is expected to benefit from DL applications, especially in predictive maintenance applications, where smart audio and video sensors can be leveraged yet kept distinct from safety-critical functions. Such separation is crucial, as it allows for improving system dependability with no impact on its safety certification. This is further supported by the development of DL in other transportation domains, such as automotive and avionics, opening for knowledge transfer opportunities and highlighting the potential of such a paradigm in railways. In order to summarize the recent state-of-the-art while inquiring about future opportunities, this paper reviews DL approaches for the analysis of data generated by acoustic and visual sensors in railway maintenance applications that have been published until August 31st, 2021. In this paper, the current state of the research is investigated and evaluated using a structured and systematic method, in order to highlight promising approaches and successful applications, as well as to identify available datasets, current limitations, open issues, challenges, and recommendations about future research directions.

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Section: A Catenary Support Device (Csd)mentioning

confidence: 99%

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

et al. 2022

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“…Here, augmented reality uses a display (mobile phone screen) to overlay digital information (veins map) onto the real world (human body parts like hands). First, the contours of detected veins are extracted by the contour tracking algorithm used in [30]. Contours are consecutive points with the same color or intensity used for analyzing shapes and object detection.…”

Section: Augmented Realitymentioning

confidence: 99%

Digital Vein Mapping Using Augmented Reality

Taha¹,

Ibrahim²,

Zayed³

2020

IJIES

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Vein detection is an important issue for the medical field. There are some commercial devices for detecting veins using infrared radiation. However, most of these commercial solutions are cost-prohibitive. Recently, veins detection has attracted much attention from research teams. The main focus is on developing real-time systems with low-cost hardware. Systems developed to reduce costs suffer from low frame rates. This, in turn, makes these systems not suitable for real-world applications. On the other hand, systems that use powerful processors to produce high frame rates suffer from high costs and a lack of mobility. In this paper, a real-time vein mapping prototype using augmented reality is proposed. The proposed prototype provides a compromised solution to produce high frame rates with a low-cost system. It consists of a USB camera attached to an Android smartphone used for real-time detection. Infrared radiation is employed to differentiate the veins using 20 Infrared Light Emitting Diodes (LEDs). The captured frames are processed to enhance vein detection using light computational algorithms to improve real-time processing and increase frame rate. Finally, the enhanced view of veins appears on the smartphone screen. Portability and economic cost are taken into consideration while developing the proposed prototype. The proposed prototype is tested with people of different ages and gender, as well as using mobile devices of different specifications. The results show a high vein detection rate and a high frame rate compared to other existing systems.

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“…The harsh environment will make the insulators vulnerable damaged, which will threaten the safety of power grid systems and the use of electricity [1]. Therefore, it is necessary to develop effective methods for insulator defect detection to ensure the safe and reliable electric power transmission [2].…”

Section: Introductionmentioning

confidence: 99%

“…Besides, there are also methods based on contour features and gray similarity matching to classify intact and defective insulators [6]. For example, Zhang and Yang [2] proposed an insulators recognition method with curved smooth and weak textured (CSWT) surfaces. Li et al [7] used the improved MPEG-7 EHD (edge histogram method) technique to recognize insulators.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

Wen

Luo

Chen

et al. 2021

Sensors

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By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.

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Catenary insulator defect detection based on contour features and gray similarity matching

Cited by 39 publications

References 19 publications

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance

Digital Vein Mapping Using Augmented Reality

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators

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