CCD Image-Based Pixel-Level Identification Model for Pavement Cracks Under Complex Noises Using Artificial Intelligence

doi:10.1109/ACCESS.2023.3305670

	CCD Image-Based Pixel-Level Identification Model for Pavement Cracks Under Complex Noises Using Artificial Intelligence
	Song, Fei 1; Zou, Yu 2; Shao, Wensha 3; Xu, Xiaoyuan 1
	2023
发表期刊	IEEE ACCESS
ISSN	2169-3536
卷号	11 页码:89733-89741
摘要	Existing manual detection methods have limitations, particularly for pavement cracks in complex backgrounds, which are manifested in low recognition accuracy, high misjudgment rate, and long time-consuming. To overcome these problems, artificial intelligence technology and charge-coupled devices (CCD) imaging technology are combined to construct an automatic identification method for pavement hidden cracks under complex background interference conditions. First, the classic semantic segmentation model U-net is selected as the basic model, and the MobileNet lightweight network is utilized to replace the encoder part of U-Net with huge parameters, to realize the lightweight of the model and improve the segmentation effect of pavement cracks. On this basis, the Atrous Channel Pyramid Attention (ACPA) mechanism is introduced into the U-net to further improve contextual information capability to focus on selectively relevant features. A pavement crack data set containing different complex and diverse crack types and background noise is used to evaluate the effectiveness and scope of application of the developed model. Quantitative evaluation results show that the developed model achieves an overall performance in the test set with a precision of 88.84%, recall of 89.76%, accuracy of 98.87%, and IoU of 89.95%, respectively. Combined with the analysis of the results of the comparison experiment and the ablation experiment, it can be inferred that the utilization of the MobileNet lightweight network to replace the encoder part of U-net can effectively construct a lightweight model while the ACPA module can effectively perform multi-scale and long-distance cross-channel interaction, help suppress useless features, strengthen useful features, and help the network learn stronger feature representations of hidden areas of pavement cracks.
关键词	Computer architecture Feature extraction Roads Decoding Computational modeling Semantics Deep learning Machine vision Surface cracks Semantic segmentation Object recognition Data models Background noise Artificial intelligence Analytical models Pavement disease machine vision deep learning damage assessment feature extraction
DOI	10.1109/ACCESS.2023.3305670
收录类别	SCIE
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001058740500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
原始文献类型	Article
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.library.ouchn.edu.cn/handle/39V7QQFX/168590
专题	国家开放大学江苏分部
通讯作者	Xu, Xiaoyuan
作者单位	1.Jiangsu Open Univ, Sch Informat Technol, Nanjing 210017, Peoples R China; 2.Jiangsu Open Univ, Sci & Technol Off, Nanjing 210036, Peoples R China; 3.Jiangsu Open Univ, Jiangsu Lifelong Educ Credit Bank Management Ctr, Nanjing 210017, Peoples R China
第一作者单位	国家开放大学江苏分部
通讯作者单位	国家开放大学江苏分部
第一作者的第一单位	国家开放大学江苏分部
推荐引用方式 GB/T 7714	Song, Fei,Zou, Yu,Shao, Wensha,et al. CCD Image-Based Pixel-Level Identification Model for Pavement Cracks Under Complex Noises Using Artificial Intelligence[J]. IEEE ACCESS,2023,11:89733-89741.
APA	Song, Fei,Zou, Yu,Shao, Wensha,&Xu, Xiaoyuan.(2023).CCD Image-Based Pixel-Level Identification Model for Pavement Cracks Under Complex Noises Using Artificial Intelligence.IEEE ACCESS,11,89733-89741.
MLA	Song, Fei,et al."CCD Image-Based Pixel-Level Identification Model for Pavement Cracks Under Complex Noises Using Artificial Intelligence".IEEE ACCESS 11(2023):89733-89741.