Applied multiresolution analysis to infrared images for defects detection in materials

Kabouri, Ahmed; Khabbazi, Abdelhamid; Youlal, H.

doi:10.1016/j.ndteint.2017.07.014

Cited by 21 publications

(9 citation statements)

References 34 publications

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“…In civil engineering, thermography is used for the non-destructive diagnostics of building structures, most often for validation of quality and integrity of building envelopes [ 20 ], in energy audits of buildings [ 21 ] and diagnostics of building materials, including wood [ 13 , 22 , 23 , 24 , 25 , 26 ]. It should be noted that active thermography is successfully used in the diagnosis of isotropic materials.…”

Section: Introductionmentioning

confidence: 99%

Active Thermography in Diagnostics of Timber Elements Covered with Polychrome

Kucharska

Jaskowska-Lemańska

2021

Materials

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The contribution of natural wood defects such as knots is an important factor influencing the strength characteristics of structural timber. This paper discusses the use of active thermography in the timber diagnostics, particularly in the determination of the knot area ratio (KAR) in elements covered with paint coatings. Moreover, on the basis of thermal images, the localization for the subsequent semi-destructive tests (SDTs) was established. Three different sources of external energy supply were used in the studies: laboratory dryer, air heater and halogen lamps. The active thermography tests were performed on elements made of three wood species (fir, pine and spruce). The specimens were covered with varying layers of paint coatings and primers, to reflect the actual condition of the historic structural elements. The obtained thermal images enabled the estimation of the KAR, due to the difference in temperature between solid wood and knots occurring therein. It should be noted that the results were affected by an external energy source and subjective judgement of the operator. Moreover, active thermography could be an effective method for the indication of the regions within which SDTs should be performed in order to properly assess the technical state of an element covered with polychrome.

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Section: Introductionmentioning

confidence: 99%

Active Thermography in Diagnostics of Timber Elements Covered with Polychrome

Kucharska

Jaskowska-Lemańska

2021

Materials

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“…Generally, conventional industrial quality monitoring involves the design of a reasonable imaging plan based on the characteristics and properties of surface defects and then uses area clipping, edge detection and other methods to isolate the main body of the defect. Finally, after extracting specific features (such as histogram of oriented gradients; (HOG); and local binary patterns (LBP)), it inputs them into the classifier or uses the threshold method to obtain the defect type [2][3][4][5][6][7][8][9][10]. For example, Xie et al found that ordinary optical inspection technology could not accurately identify semiconductor defects with large changes in scale, displacement and rotation and proposed using real noise data and simulated data to train a support vector machine (SVM) to achieve defect classification.…”

Section: Introductionmentioning

confidence: 99%

Research on a Product Quality Monitoring Method Based on Multi Scale PP-YOLO

Huang

Chen

et al. 2021

IEEE Access

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To monitor product quality in the production process in real time, this thesis proposes a quality monitoring model based on PaddlePaddle You Only Look Once (PP-YOLO). First, in the preprocessing stage, the data enhancement method and the K-means++ method are used to improve the robustness of the algorithm, and the generated anchor box can screen more refined features earlier. Second, ResNet50-vd with the deformable convolution idea is selected as the backbone of the detection model, the feature pyramid network structure and the composition of the loss function are improved, and the feature learning ability of the model is enhanced to enable it to detect multiple scales of defects. Finally, pruning is performed on the basis of the trained model to reduce the number of model parameters so that it can be deployed in industrial scenarios with limited hardware conditions. Experimental results show that the proposed quality monitoring model can meet the requirements for detection speed and accuracy in actual production, providing a new concept for the deployment of deep learning models in the industrial field.

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“…It was concluded that in order to obtain a more accurate dimensions of the defect, it was necessary to analyze the infrared thermal image with the largest temperature difference contrast or immediately thereafter by analyzing the defect results at different distances and inspection times. Kabouri A. et al [6] selected the frames that depicted the largest average surface temperature of the specimen as the candidate image frame data. However, the robustness of the image frame sequence selection methodology has been shown to be generally poor, and the proposed method of selecting an infrared sequence key frame has poor accuracy in the final defect extraction.…”

Section: Introductionmentioning

confidence: 99%

“…In the second approach of defects analysis which focuses more on digital processing of infrared images, a sparse principal component thermography (SPCT) method had been proposed to enhance the contrast of the defect induced thermal anomaly with respect to the background and the noise of the imaging sensor [27]. At present, the most mainstream method of defect representation (area) is still using the equivalent pixel ratio method [5,6], which estimates the defect area through the number of image pixels of the defect image. The equivalent length ratio for each pixel can be calibrated by using the known dimension of the specimen.…”

Section: Introductionmentioning

confidence: 99%

Automatic Extraction of Material Defect Size by Infrared Image Sequence

et al. 2020

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A typical pulsed thermography procedure results in a sequence of infrared images that reflects the evolution of temperature over time. Many features of defects, such as shape, position, and size, are derived from single image by image processing. Hence, determining the key frame from the sequence is an important problem to be solved first. A maximum standard deviation of the sensitive region method was proposed, which can identify a reasonable image frame automatically from an infrared image sequence; then, a stratagem of image composition was applied for enhancing the detection of deep defects in the key frame. Blob analysis had been adopted to acquire general information of defects such as their distributions and total number of defects. A region of interest of the defect was automatically located by its key frame combined with blob analysis. The defect information was obtained through image segmentation techniques. To obtain a robustness of results, a method of two steps of detection was proposed. The specimen of polyvinyl chloride with two artificial defects at different depths as an example was used to demonstrate how to operate the proposed method for an accurate result. At last, the proposed method was successfully adopted to examine the damage of carbon fiber-reinforced polymer. A comparative study between the proposed method and several state-of-the-art ones shows that the former is accurate and reliable and may provide a more useful and reliable tool for quality assurance in the industrial and manufacturing sectors.

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Applied multiresolution analysis to infrared images for defects detection in materials

Cited by 21 publications

References 34 publications

Active Thermography in Diagnostics of Timber Elements Covered with Polychrome

Active Thermography in Diagnostics of Timber Elements Covered with Polychrome

Research on a Product Quality Monitoring Method Based on Multi Scale PP-YOLO

Automatic Extraction of Material Defect Size by Infrared Image Sequence

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