{"title":"基于块阈值图像处理的混凝土梁裂缝识别算法研究","authors":"Wenting Qiao, Xiaoguang Wu, Wen Sun, Qiande Wu","doi":"10.32604/sdhm.2020.011479","DOIUrl":null,"url":null,"abstract":": To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven illumination and complex surface color of concrete structure, this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system. The steps in this research are as follows: 1. The crack digital images of concrete specimens with typical features were collected by using the Actis system of KURABO Co., Ltd., of Japan in the concrete beam bending test. 2. The images are segmented into blocks to distinguish backgrounds of different grayscale. 3. The maximum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented. 4. Segmentation is made to the image with 2D maximum entropy threshold segmentation method to obtain the binary image, and the target image can be obtained by screening the connected domain features of the binary image. Results have shown that compared with other algorithms, the proposed method can effectively decrease the image over-segmentation and under segmentation rates, highlight the characteristics of the target cracks, solve the problems of excessive difference between the identi fi ed length and actual length of cracks caused by background gray level change and uneven illumination, and effectively improve the recognition accuracy of bridge concrete cracks.","PeriodicalId":35399,"journal":{"name":"SDHM Structural Durability and Health Monitoring","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Research on Concrete Beam Crack Recognition Algorithm Based on Block\\nThreshold Value Image Processing\",\"authors\":\"Wenting Qiao, Xiaoguang Wu, Wen Sun, Qiande Wu\",\"doi\":\"10.32604/sdhm.2020.011479\",\"DOIUrl\":null,\"url\":null,\"abstract\":\": To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven illumination and complex surface color of concrete structure, this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system. The steps in this research are as follows: 1. The crack digital images of concrete specimens with typical features were collected by using the Actis system of KURABO Co., Ltd., of Japan in the concrete beam bending test. 2. The images are segmented into blocks to distinguish backgrounds of different grayscale. 3. The maximum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented. 4. Segmentation is made to the image with 2D maximum entropy threshold segmentation method to obtain the binary image, and the target image can be obtained by screening the connected domain features of the binary image. Results have shown that compared with other algorithms, the proposed method can effectively decrease the image over-segmentation and under segmentation rates, highlight the characteristics of the target cracks, solve the problems of excessive difference between the identi fi ed length and actual length of cracks caused by background gray level change and uneven illumination, and effectively improve the recognition accuracy of bridge concrete cracks.\",\"PeriodicalId\":35399,\"journal\":{\"name\":\"SDHM Structural Durability and Health Monitoring\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SDHM Structural Durability and Health Monitoring\",\"FirstCategoryId\":\"1087\",\"ListUrlMain\":\"https://doi.org/10.32604/sdhm.2020.011479\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SDHM Structural Durability and Health Monitoring","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.32604/sdhm.2020.011479","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Engineering","Score":null,"Total":0}
Research on Concrete Beam Crack Recognition Algorithm Based on Block
Threshold Value Image Processing
: To solve the problem that the digital image recognition accuracy of concrete structure cracks is not high under the condition of uneven illumination and complex surface color of concrete structure, this paper has proposed a block segmentation method of maximum entropy threshold based on the digital image data obtained by the ACTIS automatic detection system. The steps in this research are as follows: 1. The crack digital images of concrete specimens with typical features were collected by using the Actis system of KURABO Co., Ltd., of Japan in the concrete beam bending test. 2. The images are segmented into blocks to distinguish backgrounds of different grayscale. 3. The maximum interclass average gray difference method is used to distinguish the sub-blocks and screen out the image blocks that need to be segmented. 4. Segmentation is made to the image with 2D maximum entropy threshold segmentation method to obtain the binary image, and the target image can be obtained by screening the connected domain features of the binary image. Results have shown that compared with other algorithms, the proposed method can effectively decrease the image over-segmentation and under segmentation rates, highlight the characteristics of the target cracks, solve the problems of excessive difference between the identi fi ed length and actual length of cracks caused by background gray level change and uneven illumination, and effectively improve the recognition accuracy of bridge concrete cracks.
期刊介绍:
In order to maintain a reasonable cost for large scale structures such as airframes, offshore structures, nuclear plants etc., it is generally accepted that improved methods for structural integrity and durability assessment are required. Structural Health Monitoring (SHM) had emerged as an active area of research for fatigue life and damage accumulation prognostics. This is important for design and maintains of new and ageing structures.