Concrete spalling detection system based on semantic segmentation using deep architectures - Yasmin, Tamanna; La, Duc; La, Kien; Nguyen, Minh Tuan; La, Hung Manh (Computers & structures, 2024)
This paper presents a method for detecting the location of spalling and assessing the severity level of the spalling in concrete surfaces. The proposed method is constructed based on deep learning architectures and multi-class semantic segmentation. The proposed method can detect each pixel as a non-spalling, a deep-spalling, or a shallow-spalling. The proposed method consists of three different deep learning architectures with several encoders as backbone networks. Both qualitative and quantitative analyses show that the deep learning architecture with a certain encoder network can detect spalling with different severity levels very well. Additionally, the paper proposes a method to analyze the deep spalling areas of concrete to show their severity levels. The performance analysis shows that this approach provides very convincing results with respect to the actual affected spalling areas. The results convey that this paper achieved a higher level of performance for detecting spalling and assessing the severity of the spalling. •A deep learning architecture-based method to detect spalling in concrete surfaces is proposed.•Different encoders are used as backbone networks to optimize the deep learning encoder-decoder architecture for spalling detection.•Multi-class semantic segmentation categorizes spalling severity as “deep spalling,” “shallow spalling,” or “non-spalling”.•The performance analysis infers high precision and recall, obtained while detecting spalling in concrete surfaces.