APPLICATION OF MACHINE VISION TECHNOLOGY IN ROAD DETECTION
DOI:
https://doi.org/10.14311/CEJ.2018.04.0041Klíčová slova:
Machine vision, Road detection, Crack, Compactness, EvennessAbstrakt
Machine vision was first applied in industrial manufacturing field, and now it is also used in road detection, with the rapid development and continuous innovation of computer technology and digital image processing technology. This study provides a detailed description of the application of machine vision technology in detection of pavement crack, such as crack image acquisition, preprocessing (image de-noising and enhancement), segmentation, and recognition technology. Further the application of machine vision technology in pavement compactness and evenness was introduced. Finally, based on the application of machine vision in other aspects of road detection, hopefully, this study can provide a reference for method of road detection.
Stažení
Reference
Zhang Hong, Ying Hong., 2009. Research progress of the recognition technology of asphalt pavement crack image[J]. East China Highway, 4(178), 81-84.
Liu Wanyu, Xie Kai, Pu Zhaobang., 2007. Review of development of automatic detection system for highway pavement[J]. Journal of China & Foreign Highway, 27(2),30-33.
Qin Yahang, Su Jianhuan, Yu Rongchuan., 2016. Development of machine vision and applications[J]. Science & Technology Vision, 153-154.
Yao Guozheng, Wang Yunjiu., 1984. D.Marr and visual computing theory[J]. Automation abroad, 6,55-57.
Wang Xingjian, Qin Guofeng, Zhao Huili., 2010. Pavement crack detection method of basing on the multilevel denoising model[J]. Journal of Computer Applications, 30(6),1606-1612.
Peng Bo, Luo Nanxin, Jiang Yangsheng, Chen Cheng, K C P WANG., 2015. A denoising algorithm for pavement cracking images based on Bi-layer connectivity checking[J]. Journal of Highway and Transportation Research and Development, 32(11),7-13.
Zhang Yongqiang., 2013. Algorithm for pavement distress image denoising based on gradient enhanced diffusion[J]. Computer Science, 40(6),288-290.
Wang Gang, Wang Juan, Wang Dehua, Zhang Zhifeng, Xiao Liang, He Anzhi., 2009. The denoising algorithm for pavement images based on the statistical model in contourlet transform domain[J]. Journal of Optoelectronics·Laser, 20(10),1394-1398.
Chengdong Wu, Baihua Lu, Dongyue Chen, Li Wang., 2011. Pavement image denoising based on shearlet transform. 2011 International Conference on Electronics and Optoelectronics, 262-265.
Han Lina, Gong Guohua, Zhou Mingquan., 2012. Research on pavement image denoising using transform do-main and noise estimation[J]. Computer Engineering and Applications, 48(19),1-3.
Huang Baogui, Ma Chunmei, Lu Zhentai., 2011. New image denoising approach based on morphology[J]. Journal of Computer Applications, 31(3),757-759.
Zheng Yongheng, Cheng Jian, Cao Zongjie., 2012. SAR image denoising via improved non-local means filter[J]. Journal of Image and Graphics, 17(7),886-891.
Cui Hua, Zhou Tingjie, Guo Lu, Yuan Chao, Song Huansheng., 2014. Image enhancement using the improved partial differential equation[J]. Journal of Xidian University, 41(6),195-198.
Luo Rui, Qu Chengming., 2016. Research on image enhancement of pavement cracks based on improved Retinex algorithm[J]. Journal of Nanyang Institute of Technology, 8(6),14-18.
Li Ziqiang, Li Gang., 2011. Fuzzy enhancement algorithm of road image based on grey relational order threshold judging[J]. Journal of Hubei University of Technology, 26(3),103-109.
Liu Yuchen, Wang Guoqiang, Lin Jianrong., 2006. Image enhancement for pavement crack image based on fuzzy theory[J]. Maintenance Machinery & Construction Technology, 2,35-37.
Wang Xiaoming, Feng Xin, Dang Jianwu., 2012. Algorithm of background processing and break-point connection for pavement distress detection[J]. Journal of Lanzhou University of Technology, 38(2),63-67.
Liu Yiling, Xie Shutong., 2014. Pavement cracks detection technology based on fuzzy mathematics[J]. Journal of Jimei University(Natural Science), 19(3),224-229.
Chen Xianqiao, Yan Xinping, Chu Xiumin., 2008. Enhancement method for pavement images with shadow based on difference threshold[J]. Computer Engineering and Applications, 44(33),188-190.
Tang Lei, Zhao Chunxia, Wang Hongnan, Shao Wenze., 2007. An self-adaption fuzzy enhancement algorithm for pavement images[J]. ACTA PHOTONICA SINICA, 36(10),1943-1948.
Changxia Ma, Wengming Wang, Chunxia Zhao, Feng Di, Zhengli Zhu., 2009. Pavement cracks detection based on FDWT. IEEE International Conference on Computational Intelligence and Software Engineering, 1-4.
Liu Na, Song Weidong, Zhao Quanhua., 2015. Morphology and maximum entropy image segmentation based urban pavement cracks detection[J]. Journal of Liaoning Technical University(Natural Science), 34(1),57-61.
Zhang Lei, Ma Jian, Song Hongxun., 2009. Pavement distress detection algorithm based on tiles in wavelet domain [J]. Journal of Zhengzhou University (Engineering Science), 30(3),48-51.
Xu Zhigang, Zhao Xiangmo, Song Huansheng, Lei Tao, Wei Na., 2010. Asphalt pavement crack recognition algorithm based on histogram estimation and shape analysis[J]. Chinese Journal of Scientific Instrument, 31(10),2260-2266.
Li Jinhui., 2003. Image processing algorithm for detecting the pavement crack diseases[J]. Computer Engineering and Applications, 35,212-232.
Song Beibei, Wei Na., 2013. FCM segmentation and morphology based asphalt pavement image cracks extraction[J]. Computer Engineering and Applications, 49(4),31-34.
Xiao Xiaoming, Ma Zhi, Cai Zixing, Tang Jin., 2011. Road image segmentation based on an adaptive region growing[J]. Control Engineering, 18(3),364-368.
Li Zhun, Xu Mingming., 2016. Application of binocular vision technology in pavement crack detection of urban road[J]. Subgrade Engineering, 4,227-229.
Xu Wei, Tang Zhenmin, Xu Dan, Wu Guoxing., 2015. Integrating multi-features fusion and gestalt principles for pavement crack detection[J]. Journal of Computer-Aided Design & Computer Graphics, 27(1),147-156.
Yuan Mengxia, Sun Zhaoyun., 2015. A method of 3D surface crack identification based on target background subtraction[J]. Journal of China & Foreign Highway, 35(5),88-93.
Zhang Dejin, Li Qingquan, Chen Ying, Cao Min, He Li., 2016. Asphalt Pavement Crack Detection Based on Spatial Clustering Feature[J]. ACTA AUTOMATICA SINICA, 42(3),443-454.
Li Wei, Hu Yanju, Sha Aimin, Sun Chaoyun, Hao Xueli., 2015. Pavement crack detection based on 3D data and dual scale clustering algorithm [J]. Journal of South China University of Technology(Natural Science Edition), 43(8),99-105.
Lei Zhang, Fan Yang, Yimin Daniel Zhang, Ying Julie Zhu., 2016. Road crack detection using deep convolutional neural network. 2016 IEEE International Conference on Image Processing, IEEE.
Wang Xiaoming, Feng Xin, Dang Jianwu., 2013. Pavement crack detection method of based on multi-image and multi-resolution[J]. Journal of Lanzhou University of Technology, 39(1),76-82.
Hu Kebo, Bai Xiuqin, Xie Lei, Cao Ping., 2008. Research on surface microcosmic detection technology of asphalt pavement based on machine vision[J]. Journal of Transport Information ang Safety, 26(4),99-102.
Senthan Mathavan, Khurram Kamal, Mujib Rahman., 2015. A review of three-dimensional imaging technologies for pavement distress detection and measurements. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS(C), 16(5),2353-2362.
Roberto Medina, José Llamas, Eduardo Zalama, Jaime Gómez-García-Bermejo., 2014. Enhanced automatic detection of road surface cracks by combining 2D/3D image processing techniques. IEEE International Conference on Image Processing, IEEE,778-782.
Ou Yangaiguo, Luo Chagen, Wang Yaping., 2011. Pavement roughness detection based on shadow moire method[J]. Highway, 55(6),185-188.
Jiang Xin., 2008. The research on pavement roughness detecting and the intelligent vision for hoisting equipment base on videogrammetry[D]. Changsha: National University of defense technology.
Ma Yukun., 2013. Laser vision method measurement system for pavement evenness[D]. Tianjin: Tianjin University.
Xie Jian., 2014. Study of surface flatness detection method based on machine vision[D]. Changsha: Central South University.
Zhang Xuelian., 2016. Realtime pavement roughness measurement via weakly calibrated laser triangulation[D]. Shenyang: Shenyang University.
Li Xirong., 2015. Process method for laser image of the compacted soil based on Matlab Gui[J]. Journal of Heilongjiang Bayi Agricultural University, 27(1),85-87.
Stahování
Publikováno
Jak citovat
Číslo
Sekce
Licence
Tato práce je licencována pod Mezinárodní licencí Creative Commons Attribution-NonCommercial 4.0.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
¨
