Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction
The damage of road auxiliary facilities poses a major hidden danger to driving safety. It is urgent to study a method that can automatically detect the damage of the road auxiliary facilities and provide help for the maintenance of traffic safety auxiliary facilities. In the method for identifying t...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2022/5995999 |
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| author | Yuanshuai Dong Yanhong Zhang Yun Hou Xinlong Tong Qingquan Wu Zuofeng Zhou Yuxuan Cao |
| author_facet | Yuanshuai Dong Yanhong Zhang Yun Hou Xinlong Tong Qingquan Wu Zuofeng Zhou Yuxuan Cao |
| author_sort | Yuanshuai Dong |
| collection | DOAJ |
| description | The damage of road auxiliary facilities poses a major hidden danger to driving safety. It is urgent to study a method that can automatically detect the damage of the road auxiliary facilities and provide help for the maintenance of traffic safety auxiliary facilities. In the method for identifying the absence of road auxiliary facilities based on deep convolutional network for image segmentation and image region correction, the PointRend model based on the deep convolutional networks (CNN) is first used to achieve the pixel-level fine segmentation of the auxiliary facilities area, and then, the multiple images in the same image are segmented. In anti-glare panel area, on the largest outer polygon estimated by the convex hull algorithm, the optimal outer quadrilateral is determined according to the distance between the vertices, and then, the anti-glare panel area correction is completed by affine transformation and finally through the image one-dimensional projection mapping and adjacent shading. The distance correlation between the boards realizes the identification and positioning of the missing light-shielding board. The highway anti-glare panel missing recognition method based on deep convolution image segmentation and correction uses the vertex distance to quickly determine the external quadrilateral, which is suitable for estimating the shape of the area in a dynamic scene. After actual testing and verification, it can accurately and efficiently identify the disease of the anti-glare plate. Compared with traditional image segmentation methods, the method using the PointRend target segmentation model has better segmentation quality for target details, and it is more robust when dealing with background interference. |
| format | Article |
| id | doaj-art-0cb16496a5b04cf2a06c6c3ea1b10e2f |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-0cb16496a5b04cf2a06c6c3ea1b10e2f2025-08-20T03:54:15ZengWileyAdvances in Civil Engineering1687-80942022-01-01202210.1155/2022/5995999Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region CorrectionYuanshuai Dong0Yanhong Zhang1Yun Hou2Xinlong Tong3Qingquan Wu4Zuofeng Zhou5Yuxuan Cao6China Highway Engineering Consulting Group Company Ltd.China Highway Engineering Consulting Group Company Ltd.China Highway Engineering Consulting Group Company Ltd.China Highway Engineering Consulting Group Company Ltd.Key & Core Technology Innovation Institute of The Greater Bay AreaXi’an Institute of Optics and Precision MechanicsChina Highway Engineering Consulting Group Company Ltd.The damage of road auxiliary facilities poses a major hidden danger to driving safety. It is urgent to study a method that can automatically detect the damage of the road auxiliary facilities and provide help for the maintenance of traffic safety auxiliary facilities. In the method for identifying the absence of road auxiliary facilities based on deep convolutional network for image segmentation and image region correction, the PointRend model based on the deep convolutional networks (CNN) is first used to achieve the pixel-level fine segmentation of the auxiliary facilities area, and then, the multiple images in the same image are segmented. In anti-glare panel area, on the largest outer polygon estimated by the convex hull algorithm, the optimal outer quadrilateral is determined according to the distance between the vertices, and then, the anti-glare panel area correction is completed by affine transformation and finally through the image one-dimensional projection mapping and adjacent shading. The distance correlation between the boards realizes the identification and positioning of the missing light-shielding board. The highway anti-glare panel missing recognition method based on deep convolution image segmentation and correction uses the vertex distance to quickly determine the external quadrilateral, which is suitable for estimating the shape of the area in a dynamic scene. After actual testing and verification, it can accurately and efficiently identify the disease of the anti-glare plate. Compared with traditional image segmentation methods, the method using the PointRend target segmentation model has better segmentation quality for target details, and it is more robust when dealing with background interference.http://dx.doi.org/10.1155/2022/5995999 |
| spellingShingle | Yuanshuai Dong Yanhong Zhang Yun Hou Xinlong Tong Qingquan Wu Zuofeng Zhou Yuxuan Cao Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction Advances in Civil Engineering |
| title | Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction |
| title_full | Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction |
| title_fullStr | Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction |
| title_full_unstemmed | Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction |
| title_short | Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction |
| title_sort | damage recognition of road auxiliary facilities based on deep convolution network for segmentation and image region correction |
| url | http://dx.doi.org/10.1155/2022/5995999 |
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