Imaging Feature Analysis-Based Intelligent Laser Cleaning Using Metal Color Difference and Dynamic Weight Dispatch Corrosion Texture

Li, Jiacheng; Liu, Haoting; Shi, Lei; Lan, Jinhui

doi:10.3390/photonics7040130

Cited by 12 publications

(3 citation statements)

References 27 publications

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“…It can accurately detect, recognize and classify corroded regions in the images. Additionally, texture analysis can identify the corrosion and non-corrosion regions [181,182]. Application of SVM; water pipelines [183], underwater pipelines [184], steel bars [185], bridge cables [186], equipment [187], aircraft structures [188], wind turbine blades [189], and many more.…”

Section: Texture Analysismentioning

confidence: 99%

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Imran

Jamaludin

Ayob

et al. 2023

JMSE

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One of the biggest problems the maritime industry is currently experiencing is corrosion, resulting in short and long-term damages. Early prediction and proper corrosion monitoring can reduce economic losses. Traditional approaches used in corrosion prediction and detection are time-consuming and challenging to execute in inaccessible areas. Due to these reasons, artificial intelligence-based algorithms have become the most popular tools for researchers. This study discusses state-of-the-art artificial intelligence (AI) methods for marine-related corrosion prediction and detection: (1) predictive maintenance approaches and (2) computer vision and image processing approaches. Furthermore, a brief description of AI is described. The outcomes of this review will bring forward new knowledge about AI and the development of prediction models which can avoid unexpected failures during corrosion detection and maintenance. Moreover, it will expand the understanding of computer vision and image processing approaches for accurately detecting corrosion in images and videos.

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Section: Texture Analysismentioning

confidence: 99%

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Imran

Jamaludin

Ayob

et al. 2023

JMSE

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“…SVM can also be improved using other methods to obtain a better accuracy for corrosion detection and assessment, such as HOG [74], scale-invariant feature transform (SIFT) [75], and speeded up robust features (SURF) [76]. The corroded and non-corroded regions can be distinguished from colors [77] and textures [78] using SVM.…”

Section: Classification With Svmmentioning

confidence: 99%

Computer Vision and Image Processing Approaches for Corrosion Detection

Ali,

Jamaludin,

Imran

et al. 2023

JMSE

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Corrosion is an undesirable phenomenon resulting in material deterioration and degradation through electrochemical or chemical reactions with the surrounding environment. Additionally, corrosion presents considerable threats in both the short and long term because of its ability to create failures, leakages, and damage to materials, equipment, and environment. Despite swift technological developments, it remains difficult to determine the degrees of corrosion due to the different textures and the edgeless boundary of corrosion surfaces. Hence, there is a need to investigate the robust corrosion detection algorithms that are suitable for all degrees of corrosion. Recently, many computer vision and image processing algorithms have been developed for corrosion prediction, assessment, and detection, such as filtering, texture, color, pixelation, image enhancement, wavelet transformation, segmentation, classification, and clustering approaches. As a result, this paper reviews and discusses the state-of-the-art computer vision and image processing methods that have been developed for corrosion detection in various applications, industries, and academic research. The challenges for corrosion detection using computer vision and image processing algorithms are also explored. Finally, recommendations for future research are also detailed.

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“…In order to obtain the experimental data, a Cartesian coordinate robot for laser cleaning and image acquisition is built [40]. The output end of visible-light camera and fiber laser can be alternately fixed on the Cartesian coordinate robot system and the workpiece to be cleaned, located below the robot.…”

Section: Experimental Data Acquisitionmentioning

confidence: 99%

Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR

Liu

Shen

et al. 2022

Photonics

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In order to evaluate the effect of laser cleaning, a new method of workpiece surface roughness estimation is proposed. First, a Cartesian robot and visible-light camera are used to collect a large number of surface images of a workpiece after laser cleaning. Second, various features including the Tamura coarseness, Alexnet abstract depth, single blind/referenceless image spatial quality evaluator (BRISQUE), and enhanced gray level co-occurrence matrix (EGLCM) are computed from the images above. Third, the improved particle swarm optimization (IPSO) is used to improve the training parameters of support vector regression (SVR). The learning factor of SVR adopts the strategy of dynamic nonlinear asynchronous adaptive adjustment to improve its optimization-processing ability. Finally, both the image features and the IPSO-SVR are considered for the surface roughness estimation. Extensive experiment results show that the accuracy of the IPSO-SVR surface roughness estimation model can reach 92.0%.

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Imaging Feature Analysis-Based Intelligent Laser Cleaning Using Metal Color Difference and Dynamic Weight Dispatch Corrosion Texture

Cited by 12 publications

References 27 publications

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Application of Artificial Intelligence in Marine Corrosion Prediction and Detection

Computer Vision and Image Processing Approaches for Corrosion Detection

Laser Cleaning Surface Roughness Estimation Using Enhanced GLCM Feature and IPSO-SVR

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