Real-time monitoring of concrete crack based on deep learning algorithms and image processing techniques.

• A novel method is developed for real-time monitoring of crack dynamic expansion. • An efficient benchmarking method is established to determine crack expansion. • The YOLOv7 algorithm is improved to enhance the accuracy of crack detection. • A new attention mechanism is introduced in the DeepLabv3+ algorithm to improve crack segmentation. Crack monitoring has been a hot research topic in structural health monitoring. However, the current research on deep learning-based crack image focuses more on cracks at a certain moment and ignores the full-time crack expansion details, which are crucial for more reasonable evaluation and safety quantification of concrete structures. This paper proposes a new method based on the combination of improved You Only Look Once v7 (YOLOv7) algorithm, crack expansion benchmark method, improved DeepLabv3+ algorithm, and image processing technology to monitor the whole process of crack development, including real-time crack recognition and real-time monitoring of crack dynamic expansion. The precision of the improved detection algorithm can be improved by a maximum of 5.34%, and the mean intersection over union (mIoU) of the improved segmentation algorithm can be improved by 0.15%, resulting in better segmentation results. The experimental results show that this method can efficiently and accurately achieve real-time tracking of crack dynamic expansion, especially for monitoring of tiny cracks. [ABSTRACT FROM AUTHOR]