Internal fault classification using Artificial Neural Network

Shafi'i, Mohd Anuar; Hamzah, Noraliza

doi:10.1109/peoco.2010.5559176

Cited by 28 publications

(18 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[7][8][9][10] For diagnosis of thermal defects, regions of interest (ROIs) are selected by feature descriptions. Various intelligent techniques, such as neural network (NN), 11,12 support vector machine (SVM), 13,14 and neurofuzzy algorithm, 15 have been used for the classification. In literature, the simplest approach to distinguish hot/cold spot regions in the thermal image of a building is to use statistical methods and morphological image processing technique in conjunction with quantitative analyses on the inspection results.…”

Section: Review On Approaches For Hollowness Assessmentmentioning

confidence: 99%

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

Liu

Yan

Maruvanchery

et al. 2017

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

We propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.

show abstract

Section: Review On Approaches For Hollowness Assessmentmentioning

confidence: 99%

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

Liu

Yan

Maruvanchery

et al. 2017

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…Assessing the electrical equipments by analyzing its statistical temperature distribution of each detected region is more practical because the actual severity level of the equipments could be accurately predicted. Another straightforward method of evaluation is by analyzing the real temperature values for each pixel in the image by extracting directly from its RGB data [52]. This method is quite simple but has a problem with high processing time due to the large feature vectors to be computed by a classifier algorithm.…”

Section: Automated Diagnostic Systemmentioning

confidence: 99%

Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography

Jadin

Taib

2012

Infrared Physics & Technology

256

View full text Add to dashboard Cite

“…The real temperature values for each pixel in the image can be extracted directly from its RGB data. This method is quite straightforward but has a problem with high processing time due to the large feature vectors to be computed by an artificial neural network (ANN) algorithm (Shafi'i & Hamzah, 2010). The previous research with various hotspot detection techniques and fault classification method is summarized in (Rahmani et al, 2010) Thresholding SVM (Wretman, 2006), (Smedberg, 2006) Finding Instead of using classical bottom-up approach, Wretman (Wretman, 2006) and Smedberg (Smedberg, 2006) have successfully segmented the IRT image of electrical installation by using the top-down approach of image processing method.…”

Section: Distance and Anglementioning

confidence: 99%

Thermal Imaging for Enhancing Inspection Reliability: Detection and Characterization

Taib¹,

Jadin²,

Kabir³

2012

Infrared Thermography

View full text Add to dashboard Cite

Internal fault classification using Artificial Neural Network

Cited by 28 publications

References 1 publication

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

Transfer learning on convolutional activation feature as applied to a building quality assessment robot

Recent progress in diagnosing the reliability of electrical equipment by using infrared thermography

Thermal Imaging for Enhancing Inspection Reliability: Detection and Characterization

Contact Info

Product

Resources

About