Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation
The detection of building wall surface defects is of great significance to eliminate potential safety hazards. In this paper, a research on building wall design defect image recognition based on partial differential equation is proposed. Collect the image data of building surface defects, sample and...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Mathematical Physics |
| Online Access: | http://dx.doi.org/10.1155/2021/1229660 |
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| _version_ | 1832565262433386496 |
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| author | Xiwen Yu Kai Wang Shaoxuan Wang |
| author_facet | Xiwen Yu Kai Wang Shaoxuan Wang |
| author_sort | Xiwen Yu |
| collection | DOAJ |
| description | The detection of building wall surface defects is of great significance to eliminate potential safety hazards. In this paper, a research on building wall design defect image recognition based on partial differential equation is proposed. Collect the image data of building surface defects, sample and quantify the collected images, and preprocess the defect images such as digital threshold segmentation, filtering, and enhancement. Then, the improved partial differential equation is used to recognize the image as a whole. The second-order partial differential diffusion equation and the fourth-order partial differential equation are used to recognize the high-frequency and low-frequency bands of the image, respectively. The kernel principal component analysis algorithm is used to transfer the overall image input space to the high-dimensional feature space. The kernel function is used to calculate the inner product in different subband images of the high-dimensional feature space to reduce the dimension of the overall image. The processed coefficients are inversely transformed by nondownsampling contour wave to realize the overall image recognition and ensure that the edge information of the source image does not disappear. Experimental results show that compared with other algorithms, the proposed algorithm has better effect and better stability. |
| format | Article |
| id | doaj-art-d1350fbe1adb4ae3873d2f2556d8b447 |
| institution | Kabale University |
| issn | 1687-9120 1687-9139 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Mathematical Physics |
| spelling | doaj-art-d1350fbe1adb4ae3873d2f2556d8b4472025-02-03T01:08:52ZengWileyAdvances in Mathematical Physics1687-91201687-91392021-01-01202110.1155/2021/12296601229660Research on Image Recognition of Building Wall Design Defects Based on Partial Differential EquationXiwen Yu0Kai Wang1Shaoxuan Wang2School of Arts and Media, Hefei Normal University, Hefei 230601, ChinaSchool of Science, Anhui Agricultural University, Hefei 230036, ChinaShanghai Eigencomm Technologies Ltd., Shanghai 201210, ChinaThe detection of building wall surface defects is of great significance to eliminate potential safety hazards. In this paper, a research on building wall design defect image recognition based on partial differential equation is proposed. Collect the image data of building surface defects, sample and quantify the collected images, and preprocess the defect images such as digital threshold segmentation, filtering, and enhancement. Then, the improved partial differential equation is used to recognize the image as a whole. The second-order partial differential diffusion equation and the fourth-order partial differential equation are used to recognize the high-frequency and low-frequency bands of the image, respectively. The kernel principal component analysis algorithm is used to transfer the overall image input space to the high-dimensional feature space. The kernel function is used to calculate the inner product in different subband images of the high-dimensional feature space to reduce the dimension of the overall image. The processed coefficients are inversely transformed by nondownsampling contour wave to realize the overall image recognition and ensure that the edge information of the source image does not disappear. Experimental results show that compared with other algorithms, the proposed algorithm has better effect and better stability.http://dx.doi.org/10.1155/2021/1229660 |
| spellingShingle | Xiwen Yu Kai Wang Shaoxuan Wang Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation Advances in Mathematical Physics |
| title | Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation |
| title_full | Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation |
| title_fullStr | Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation |
| title_full_unstemmed | Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation |
| title_short | Research on Image Recognition of Building Wall Design Defects Based on Partial Differential Equation |
| title_sort | research on image recognition of building wall design defects based on partial differential equation |
| url | http://dx.doi.org/10.1155/2021/1229660 |
| work_keys_str_mv | AT xiwenyu researchonimagerecognitionofbuildingwalldesigndefectsbasedonpartialdifferentialequation AT kaiwang researchonimagerecognitionofbuildingwalldesigndefectsbasedonpartialdifferentialequation AT shaoxuanwang researchonimagerecognitionofbuildingwalldesigndefectsbasedonpartialdifferentialequation |