Semantic Recognition and Location of Cracks by Fusing Cracks Segmentation and Deep Learning
Qing An,
Xijiang Chen,
Xiaoyan Du,
Jiewen Yang,
Shusen Wu,
Ya Ban
Affiliations
Qing An
School of Artificial Intelligence, Wuchang University of Technology, Wuhan, Hubei 430223, China
Xijiang Chen
School of Artificial Intelligence, Wuchang University of Technology, Wuhan, Hubei 430223, China
Xiaoyan Du
School of Safety and Emergency Management, Wuhan University of Technology, Wuhan, Hubei 430079, China
Jiewen Yang
School of Safety and Emergency Management, Wuhan University of Technology, Wuhan, Hubei 430079, China
Shusen Wu
State Key Laboratory of Materials Processing and Die and Mould Technology, School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
Ya Ban
Chongqing Measurement Quality Examination Research Institute, Chongqing 404100, China
For a long time, cracks can appear on the surface of concrete, resulting in a number of safety problems. Traditional manual detection methods not only cost money and time but also cannot guarantee high accuracy. Therefore, a recognition method based on the combination of convolutional neural network and cluster segmentation is proposed. The proposed method realizes the accurate identification of concrete surface crack image under complex background and improves the efficiency of concrete surface crack identification. The research results show that the proposed method not only classifies crack and noncrack efficiently but also identifies cracks in complex backgrounds. The proposed method has high accuracy in crack recognition, which is at least 97.3% and even up to 98.6%.
