Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures
Underwater structures are crucial for national economic and social development. However, because of their complex environment, they are susceptible to damage during service. This damage should be prevented to minimize casualties and economic loss. Therefore, this study investigates the problems of d...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/8760324 |
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| _version_ | 1832550480648077312 |
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| author | Longsheng Bao Chunyan Zhao Xingwei Xue Ling Yu |
| author_facet | Longsheng Bao Chunyan Zhao Xingwei Xue Ling Yu |
| author_sort | Longsheng Bao |
| collection | DOAJ |
| description | Underwater structures are crucial for national economic and social development. However, because of their complex environment, they are susceptible to damage during service. This damage should be prevented to minimize casualties and economic loss. Therefore, this study investigates the problems of disease identification and area statistics of underwater structures. To this end, the Dark-Retinex (DR) algorithm that can enhance the image of underwater structure defects is proposed. The algorithm consists of a combination of a dark channel algorithm and the Retinex algorithm. This study analyzes the current research status of underwater image processing technology, designs the overall framework of the DR algorithm, and uses the underwater structure disease image to verify the algorithm. Comparing the effect of the image with only the dark channel defogging and DR algorithm processing, the DR algorithm is observed to achieve “defogging” processing of underwater structural disease images to achieve better enhancement effects. Moreover, for accurate disease area statistics, the binary morphology and optimal threshold segmentation theories are combined to perform disease edge screening and remove interference information. Finally, accurate statistics of the proportion of diseased pixels are achieved, as well as the quantitative detection of surface diseases of underwater structures. After actual operational verification, the improved image dehazing and parallel boundary screening algorithms can achieve better application results to detect underwater structure defects and disease statistics. The objective evaluation shows that the DR algorithm facilitates image processing, can obtain relatively high-quality target images, and can solve the problems of time-consuming and labor-intensive detection of underwater structures, with significant risks and limitations. This helps pave the way for (1) the actual engineering of surface structure detection of underwater structures, (2) future storage in the database and assessment of hazard levels, and (3) a guide for engineering technicians to take corresponding maintenance measures. |
| format | Article |
| id | doaj-art-435827e1494248b18302f32c002a6d05 |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-435827e1494248b18302f32c002a6d052025-02-03T06:06:32ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422020-01-01202010.1155/2020/87603248760324Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater StructuresLongsheng Bao0Chunyan Zhao1Xingwei Xue2Ling Yu3School of Traffic Engineering, Shenyang Jianzhu University, Shenyang 110168, Liaoning, ChinaSchool of Traffic Engineering, Shenyang Jianzhu University, Shenyang 110168, Liaoning, ChinaSchool of Traffic Engineering, Shenyang Jianzhu University, Shenyang 110168, Liaoning, ChinaSchool of Traffic Engineering, Shenyang Jianzhu University, Shenyang 110168, Liaoning, ChinaUnderwater structures are crucial for national economic and social development. However, because of their complex environment, they are susceptible to damage during service. This damage should be prevented to minimize casualties and economic loss. Therefore, this study investigates the problems of disease identification and area statistics of underwater structures. To this end, the Dark-Retinex (DR) algorithm that can enhance the image of underwater structure defects is proposed. The algorithm consists of a combination of a dark channel algorithm and the Retinex algorithm. This study analyzes the current research status of underwater image processing technology, designs the overall framework of the DR algorithm, and uses the underwater structure disease image to verify the algorithm. Comparing the effect of the image with only the dark channel defogging and DR algorithm processing, the DR algorithm is observed to achieve “defogging” processing of underwater structural disease images to achieve better enhancement effects. Moreover, for accurate disease area statistics, the binary morphology and optimal threshold segmentation theories are combined to perform disease edge screening and remove interference information. Finally, accurate statistics of the proportion of diseased pixels are achieved, as well as the quantitative detection of surface diseases of underwater structures. After actual operational verification, the improved image dehazing and parallel boundary screening algorithms can achieve better application results to detect underwater structure defects and disease statistics. The objective evaluation shows that the DR algorithm facilitates image processing, can obtain relatively high-quality target images, and can solve the problems of time-consuming and labor-intensive detection of underwater structures, with significant risks and limitations. This helps pave the way for (1) the actual engineering of surface structure detection of underwater structures, (2) future storage in the database and assessment of hazard levels, and (3) a guide for engineering technicians to take corresponding maintenance measures.http://dx.doi.org/10.1155/2020/8760324 |
| spellingShingle | Longsheng Bao Chunyan Zhao Xingwei Xue Ling Yu Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures Advances in Materials Science and Engineering |
| title | Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures |
| title_full | Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures |
| title_fullStr | Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures |
| title_full_unstemmed | Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures |
| title_short | Improved Dark Channel Defogging Algorithm for Defect Detection in Underwater Structures |
| title_sort | improved dark channel defogging algorithm for defect detection in underwater structures |
| url | http://dx.doi.org/10.1155/2020/8760324 |
| work_keys_str_mv | AT longshengbao improveddarkchanneldefoggingalgorithmfordefectdetectioninunderwaterstructures AT chunyanzhao improveddarkchanneldefoggingalgorithmfordefectdetectioninunderwaterstructures AT xingweixue improveddarkchanneldefoggingalgorithmfordefectdetectioninunderwaterstructures AT lingyu improveddarkchanneldefoggingalgorithmfordefectdetectioninunderwaterstructures |