Semantic Segmentation of Defects in Infrared Thermographic Images of Highly Damaged Concrete Structures

Pozzer, Sandra; Azar, Ehsan Rezazadeh; Rosa, Francisco Dalla; Pravia, Zacarias Martin Chamberlain

doi:10.1061/(asce)cf.1943-5509.0001541

Cited by 54 publications

(22 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As described above, the WTDR criteria of detectable delamination are influenced by test methods, the presence of solar irradiation, measurement cycle, and test regions. [10,56,82,85,87,89,[100][101][102][103]132,133,143,[145][146][147]149,157,159,164,167,170,174,186].…”

Section: Width To Depth Ratiomentioning

confidence: 99%

“…Hiasa et al [88] showed convergence at 1 cm thickness by the numerical simulation. [10,56,82,85,87,89,[100][101][102][103]132,133,143,[145][146][147]149,157,159,164,167,170,174,186].…”

Section: Thicknessmentioning

confidence: 99%

“…Synthesized data have following terms: target object is ordinary concrete; analysis method is one-time data analysis; width represents diameter or shortest side of delamination; detectability is determined according to each study. Sources are[10,56,82,85,87,89,[100][101][102][103]132,133,143,[145][146][147]149,157,159,164,167,170,174,186].…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings

Tomita

Chew

2022

Sensors

View full text Add to dashboard Cite

This paper provides a comprehensive review on the use of infrared thermography to detect delamination on infrastructures and buildings. Approximately 200 pieces of relevant literature were evaluated, and their findings were summarized. The factors affecting the accuracy and detectability of infrared thermography were consolidated and discussed. Necessary measures to effectively capture latent defects at the early stage of delamination before crack formation were investigated. The results of this study could be used as the benchmarks for setting standardized testing criteria as well as for comparison of results for future works on the use of infrared thermography for detection of delamination on infrastructures and buildings.

show abstract

Section: Width To Depth Ratiomentioning

confidence: 99%

“…Hiasa et al [88] showed convergence at 1 cm thickness by the numerical simulation. [10,56,82,85,87,89,[100][101][102][103]132,133,143,[145][146][147]149,157,159,164,167,170,174,186].…”

Section: Thicknessmentioning

confidence: 99%

See 1 more Smart Citation

A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings

Tomita

Chew

2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The data acquisition campaigns were performed in 4 concrete structures: a concrete dam, 2 highway bridges, and 1 railway overpass in Canada. Part of the current dataset was collected and analyzed in a previous study [2]. Four different thermal cameras were used to collect the data, and all the devices were equipped with both thermal and optical sensors; therefore, each capture provides an optical and thermal frame with the same field of view, allowing further image registration and fusion.…”

Section: Methodsmentioning

confidence: 99%

“…Computer vision methods combined with remote sensors represent a promising solution for the automatic inspection of concrete structures [1,2]. The goal of using such advanced non-destructive testing and evaluation (NDTE) methods is to more efficiently evaluate the inspection of large infrastructures [3].…”

Section: Introductionmentioning

confidence: 99%

Defect Segmentation in Concrete Structures Combining Registered Infrared and Visible Images: A Comparative Experimental Study

Pozzer

Souza

Hena

et al. 2021

The 16th International Workshop on Advanced Infrared Technology &Amp;amp; Applications

Self Cite

View full text Add to dashboard Cite

This study investigates the semantic segmentation of common concrete defects when using different imaging modalities. One pre-trained Convolutional Neural Network (CNN) model was trained via transfer learning and tested to detect concrete defect indications, such as cracks, spalling, and internal voids. The model’s performance was compared using datasets of visible, thermal, and fused images. The data were collected from four different concrete structures and built using four infrared cameras that have different sensitivities and resolutions, with imaging campaigns conducted during autumn, summer, and winter periods. Although specific defects can be detected in monomodal images, the results demonstrate that a larger number of defect classes can be accurately detected using multimodal fused images with the same viewpoint and resolution of the single-sensor image.

show abstract

Lightweight Semantic Segmentation Convolutional Neural Network Based on SKNet

Zhong

Zhao

Liu

2021

Proceedings of the 11th International Conference on Computer Engineering and Networks

View full text Add to dashboard Cite

Semantic Segmentation of Defects in Infrared Thermographic Images of Highly Damaged Concrete Structures

Cited by 54 publications

References 32 publications

A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings

A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings

Defect Segmentation in Concrete Structures Combining Registered Infrared and Visible Images: A Comparative Experimental Study

Lightweight Semantic Segmentation Convolutional Neural Network Based on SKNet

Contact Info

Product

Resources

About