Automatic corrosion estimation using galvanized steel images on power transmission towers

Tsutsumi, Fujio; Murata, Hiroshi; Onoda, Takashi; Oguri, O.; Tanaka, Hidehiko

doi:10.1109/td-asia.2009.5356961

Cited by 12 publications

(9 citation statements)

References 4 publications

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“…In addition to the detection of towers, there is a need to identify defects in its components. A method for estimating corrosion on towers was presented in (Tsutsumi et al, 2009). It was based on a support vector machine using the radial basis function kernel.…”

Section: Detection Of Towersmentioning

confidence: 99%

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

Odo

McKenna

Flynn

et al. 2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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Visual inspection of electricity transmission and distribution networks relies on flying a helicopter around energized high voltage towers for image collection. The sensed data is taken offline and screened by skilled personnel for faults. This poses high risk to the pilot and crew and is highly expensive and inefficient. This paper reviews work targeted at detecting components of electricity transmission and distribution lines with attention to unmanned aerial vehicle (UAV) platforms. The potential of deep learning as the backbone of image data analysis was explored. For this, we used a new dataset of high resolution aerial images of medium-to-low voltage electricity towers. We demonstrated that reliable classification of towers is feasible using deep learning methods with very good results.

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Section: Detection Of Towersmentioning

confidence: 99%

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

Odo

McKenna

Flynn

et al. 2020

Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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“…The resulting method provides an accuracy above 97%. Tsutsumi et al (2009) also use a SVM in their approach. It presents a system to categorize images from metallic power transmission towers depending on its deterioration degree.…”

Section: Algorithms For Corrosion Detectionmentioning

confidence: 99%

On the use of robots and vision technologies for the inspection of vessels: A survey on recent advances

Bonnín-Pascual¹,

Ortiz²

2019

Ocean Engineering

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Vessels are widely used for transporting goods around the world. All cargo vessels are affected by two main defective situations, namely cracks and corrosion. To prevent major damage/accidents, intensive inspection schemes must be carried out periodically, identifying the affected plates for a subsequent repair/replacement. These inspections are performed at a great cost due to the arrangements that allow human inspectors to reach any point of the vessel structure while guaranteeing their physical integrity and respecting all the stipulated safety measures. Technological advances can provide alternatives to facilitate the vessel inspection and reduce the associated cost. This paper surveys approaches which can contribute to the reengineering process of vessel visual inspection focusing on two main aspects: robotic platforms which can be used for the visual inspection of vessels, and computer vision algorithms for the detection of cracks and/or corrosion in images. The different approaches found in the literature are reviewed and classified regarding their key features, what allows identifying the main trends which are being applied so far and those which could mean an improvement in the current visual inspection.

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“…Thus, the proposed method can estimate various kinds of images correctly. In general estimation methods such as [21], in order to cope with such variations, the targets in images are clipped manually. Although these are semi-automatic methods, our method is a fully automatic estimation method since we can input the original images into our method directly.…”

Section: B Performance Evaluationmentioning

confidence: 99%

“…Transmission towers are constructed by using galvanized steel and coated with rust-preventive paint to prevent corrosion [24]. However, recoating is necessary because the rust-preventive paint deteriorates over time [21]. Thus, inspectors have to determine the levels of deterioration of the transmission towers by observing the surfaces of towers and estimating the levels of deterioration based on their experience and knowledge [25], [26].…”

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confidence: 99%

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Estimation of Deterioration Levels of Transmission Towers via Deep Learning Maximizing Canonical Correlation Between Heterogeneous Features

Maeda

Takahashi

Ogawa

et al. 2018

IEEE J. Sel. Top. Signal Process.

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This paper presents estimation of deterioration levels of transmission towers via deep learning maximizing the canonical correlation between heterogeneous features. In the proposed method, we newly construct a correlation-maximizing deep extreme learning machine based on a local receptive field (CMDELM-LRF). For accurate deterioration level estimation, it is necessary to obtain semantic information that effectively represents deterioration levels. However, since the amount of training data for transmission towers is small, it is difficult to perform feature transformation by using many hidden layers such as general deep learning methods. In CMDELM-LRF, one hidden layer, which maximizes the canonical correlation between visual features and text features obtained from inspection text data, is newly inserted. Specifically, by using projections obtained by maximizing the canonical correlation as weight parameters of the hidden layer, feature transformation for extracting semantic information is realized without designing many hidden layers. This is the main contribution of this paper. Consequently, CMDELM-LRF realizes accurate deterioration level estimation from a small amount of training data. Index Terms-Deterioration level estimation, deep extreme learning machine, canonical correlation analysis. I. Introduction A LL countries have critical infrastructures such as power grids, railways, tunnels, bridges and transmission towers. In order to maintain these infrastructures, visual inspection has usually been performed by inspectors. Visual inspection is a labor-intensive and time-consuming process [1]. In order to reduce costs, new techniques for supporting maintenance inspection are required [2]-[6]. Some methods have been proposed for supporting various inspection tasks for infrastructures including distress classification [7], [8], detection of specific distresses [9]-[17], analysis of surface status [18]-[20] and deterioration level estimation [21], [22]. Support for maintenance inspection of transmission towers is important since inspection is performed by inspectors ascending towers, which is a dangerous inspection task [23].

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Automatic corrosion estimation using galvanized steel images on power transmission towers

Cited by 12 publications

References 4 publications

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

Towards the Automatic Visual Monitoring of Electricity Pylons from Aerial Images

On the use of robots and vision technologies for the inspection of vessels: A survey on recent advances

Estimation of Deterioration Levels of Transmission Towers via Deep Learning Maximizing Canonical Correlation Between Heterogeneous Features

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