Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction | |
Dong, Yuanshuai3,4,5; Zhang, Yanhong3,4,5; Hou, Yun3,4,5; Tong, Xinlong3,4,5; Wu, Qingquan2; Zhou, Zuofeng1; Cao, Yuxuan3,4,5 | |
刊名 | ADVANCES IN CIVIL ENGINEERING |
2022-04-12 | |
卷号 | 2022 |
ISSN号 | 1687-8086;1687-8094 |
DOI | 10.1155/2022/5995999 |
产权排序 | 5 |
英文摘要 | 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. |
语种 | 英语 |
出版者 | HINDAWI LTD |
WOS记录号 | WOS:000791721000002 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/95873] |
专题 | 西安光学精密机械研究所_动态光学成像研究室 |
通讯作者 | Tong, Xinlong |
作者单位 | 1.CAS Ind Dev Co Ltd, Xian Inst Opt & Precis Mech, Xian 710019, Shaanxi, Peoples R China 2.Key & Core Technol Innovat Inst Greater Bay Area, Guangzhou 510535, Guangdong, Peoples R China 3.Res & Dev Ctr Transport Ind Technol Mat & Equipmen, Beijing 100089, Peoples R China 4.China Commun Construct Co Ltd, Res & Dev Ctr Highway Pavement Maintenance Technol, Res & Dev Ctr Highway Pavement Maintenance Technol, Beijing 100089, Peoples R China 5.China Highway Engn Consulting Grp Co Ltd, Beijing 100089, Peoples R China |
推荐引用方式 GB/T 7714 | Dong, Yuanshuai,Zhang, Yanhong,Hou, Yun,et al. Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction[J]. ADVANCES IN CIVIL ENGINEERING,2022,2022. |
APA | Dong, Yuanshuai.,Zhang, Yanhong.,Hou, Yun.,Tong, Xinlong.,Wu, Qingquan.,...&Cao, Yuxuan.(2022).Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction.ADVANCES IN CIVIL ENGINEERING,2022. |
MLA | Dong, Yuanshuai,et al."Damage Recognition of Road Auxiliary Facilities Based on Deep Convolution Network for Segmentation and Image Region Correction".ADVANCES IN CIVIL ENGINEERING 2022(2022). |
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