An Automatic Extraction Method of Rebar Processing Information Based on Digital Image
Reinforced steel is one of the most important building materials in civil engineering and improving the intelligence of steel reinforcement engineering can greatly promote the intelligent development of the construction industry. This research addressed the problems of the slow speed and poor accura...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/16/2974 |
| _version_ | 1797409061387370496 |
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| author | Zhaoxi Ma Qin Zhao Yiyun Zhu Tianyou Cang Xinhong Hei |
| author_facet | Zhaoxi Ma Qin Zhao Yiyun Zhu Tianyou Cang Xinhong Hei |
| author_sort | Zhaoxi Ma |
| collection | DOAJ |
| description | Reinforced steel is one of the most important building materials in civil engineering and improving the intelligence of steel reinforcement engineering can greatly promote the intelligent development of the construction industry. This research addressed the problems of the slow speed and poor accuracy of manually extracting rebar processing information, which leads to a low degree of rebar processing intelligence. Firstly, based on digital image processing technology, image preprocessing methods such as binarization and grayscale were used to eliminate redundant information in a detail drawing of a rebar. An image segmentation method based on pixel statistics was proposed to store the geometric and non-geometric information of the detail drawing of the rebar separately. Next, the bending angle was extracted by line thinning and corner detection, and the bending direction of the steel bar was determined based on the mathematical characteristics of the vector product. Finally, the non-geometric information was extracted by combining the morphological algorithm and the Optical Character Recognition (OCR) engine. According to the characteristics of the information sequence, an information mapping method was proposed to realize the integration of geometric and non-geometric information. The applicability and accuracy of this method for extracting the steel bar’s information were tested by experiments, and it was shown that the method also provides a theoretical basis for realizing the intelligentization and informatization of steel bar processing. |
| first_indexed | 2024-03-09T04:07:54Z |
| format | Article |
| id | doaj.art-9548e8080f6a4bb69e8f86e031534920 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T04:07:54Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-9548e8080f6a4bb69e8f86e0315349202023-12-03T14:03:43ZengMDPI AGMathematics2227-73902022-08-011016297410.3390/math10162974An Automatic Extraction Method of Rebar Processing Information Based on Digital ImageZhaoxi Ma0Qin Zhao1Yiyun Zhu2Tianyou Cang3Xinhong Hei4Department of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, ChinaDepartment of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, ChinaDepartment of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, ChinaDepartment of Civil Engineering and Architecture, Xi’an University of Technology, Xi’an 710048, ChinaDepartment of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, ChinaReinforced steel is one of the most important building materials in civil engineering and improving the intelligence of steel reinforcement engineering can greatly promote the intelligent development of the construction industry. This research addressed the problems of the slow speed and poor accuracy of manually extracting rebar processing information, which leads to a low degree of rebar processing intelligence. Firstly, based on digital image processing technology, image preprocessing methods such as binarization and grayscale were used to eliminate redundant information in a detail drawing of a rebar. An image segmentation method based on pixel statistics was proposed to store the geometric and non-geometric information of the detail drawing of the rebar separately. Next, the bending angle was extracted by line thinning and corner detection, and the bending direction of the steel bar was determined based on the mathematical characteristics of the vector product. Finally, the non-geometric information was extracted by combining the morphological algorithm and the Optical Character Recognition (OCR) engine. According to the characteristics of the information sequence, an information mapping method was proposed to realize the integration of geometric and non-geometric information. The applicability and accuracy of this method for extracting the steel bar’s information were tested by experiments, and it was shown that the method also provides a theoretical basis for realizing the intelligentization and informatization of steel bar processing.https://www.mdpi.com/2227-7390/10/16/2974rebarprocessing informationdigital image processingcorner detection |
| spellingShingle | Zhaoxi Ma Qin Zhao Yiyun Zhu Tianyou Cang Xinhong Hei An Automatic Extraction Method of Rebar Processing Information Based on Digital Image Mathematics rebar processing information digital image processing corner detection |
| title | An Automatic Extraction Method of Rebar Processing Information Based on Digital Image |
| title_full | An Automatic Extraction Method of Rebar Processing Information Based on Digital Image |
| title_fullStr | An Automatic Extraction Method of Rebar Processing Information Based on Digital Image |
| title_full_unstemmed | An Automatic Extraction Method of Rebar Processing Information Based on Digital Image |
| title_short | An Automatic Extraction Method of Rebar Processing Information Based on Digital Image |
| title_sort | automatic extraction method of rebar processing information based on digital image |
| topic | rebar processing information digital image processing corner detection |
| url | https://www.mdpi.com/2227-7390/10/16/2974 |
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