Defect detection in thermal image for nondestructive evaluation of petrochemical equipments

Heriansyah, Rudi; Abu-Bakar, S. A. R.

doi:10.1016/j.ndteint.2009.06.008

Cited by 42 publications

(28 citation statements)

References 25 publications

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“…Existing literature contains a variety of these segmentation methods applied in the domain of NDT. Many of these methods are designed for a particular application, such as the detection of weld defects (Alaknanda et al, ; Vilar et al., ; Yazid et al., ; Kasban et al., ) or pipe deterioration (Peska, ; Liu et al., ), and/or for particular image sources, such as optical (Yazid et al., ), thermal (Abdel‐Qader et al., ; Liu et al., ; Yishuo and Jer‐Wei, ; Heriansyah and Abu‐Bakar, ), ultrasonic (Molero et al., ; D'Orazio et al., ), and radiography (Alaknanda et al, ; Vilar et al., ; Yazid et al., ; Kasban et al., ). As such, while these techniques may be effective for their designated purposes, they are understandably unlikely to perform well when applied to richly detailed, high‐resolution optical images of a broad range of surface types and damage forms in complex natural scenes.…”

Section: Introductionmentioning

confidence: 99%

Regionally Enhanced Multiphase Segmentation Technique for Damaged Surfaces

O’Byrne

Ghosh

Schoefs

et al. 2014

Computer aided Civil Eng

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Imaging‐based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil infrastructure. The technique is successfully applied to a corroding infrastructure component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering‐based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique.

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Section: Introductionmentioning

confidence: 99%

Regionally Enhanced Multiphase Segmentation Technique for Damaged Surfaces

O’Byrne

Ghosh

Schoefs

et al. 2014

Computer aided Civil Eng

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“…The study in Ref. 27 uses local intensity operation for defect detection in thermal images. We adapt this operator, which brightens the bright pixels and darkens the dark pixels, for our purpose of segmenting pixels potentially belonging to people as follows: For a pixel I(x,y) in thermal image, denoted with z 0 , Z value is calculated as the product of the pixel and its eight neighbours (z 1 to z 8 ):…”

Section: Local Intensity Operation For Thermal Imagesmentioning

confidence: 99%

A multimodal approach for individual tracking of people and their belongings

Beyan

Temizel

2015

The Imaging Science Journal

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In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in visible images) is shown to result in better object detection performance. We use adaptive background modeling and local intensity operation for object detection and the mean-shift tracking algorithm for fully automatic tracking. Trackers are refreshed to resolve potential problems which may occur due to the changes in object's size, shape and to handle occlusion-split and to detect newly emerging objects as well as objects that leave the scene. The proposed scheme is applied to the abandoned object detection problem and the results are compared with the state of art methods. The results show that the proposed method facilitate individual tracking of objects for various applications, and provide lower false alarm rates compared to the state of art methods when applied to the abandoned object detection problem.

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“…In this approach, thresholding was selected for a primary segmentation of the obtained thermal images, due to its intuitive and simple implementation (Heriansyah and Abu-Bakar 2009 Figure 16 illustrates the output images from a single thresholding process that was provided in Matlab R2009a environment, with a threshold L th = 160. The latter was selected, after several trials, as the optimum threshold value that accomplishes a fair separation of unwanted regions in the majority of the tested thermal images, without cutting out any ROI.…”

Section: Canny Edge Detection Implementationmentioning

confidence: 99%

Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements

Tsanakas

Chrysostomou

Botsaris

et al. 2013

International Journal of Sustainable Energy

157

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Today, conventional condition monitoring of installed, operating photovoltaic (PV) modules is mainly based on electrical measurements and performance evaluation. However, such practices exhibit restricted fault-detection ability. This study proposes the use of standard thermal image processing and the Canny edge detection operator as diagnostic tools for module-related faults that lead to hot-spot heating effects. The intended techniques were applied on thermal images of defective PV modules, from several field infrared thermographic measurements conducted during this study. The whole approach provided promising results with the detection of hot-spot formations that were diagnosed to specific defective cells in each inspected module. These evolving hot spots lead to abnormally low performance of the PV modules, a fact that is also validated by the manufacturer's standard electrical tests.

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Defect detection in thermal image for nondestructive evaluation of petrochemical equipments

Cited by 42 publications

References 25 publications

Regionally Enhanced Multiphase Segmentation Technique for Damaged Surfaces

Regionally Enhanced Multiphase Segmentation Technique for Damaged Surfaces

A multimodal approach for individual tracking of people and their belongings

Fault diagnosis of photovoltaic modules through image processing and Canny edge detection on field thermographic measurements

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