Recognition of concrete microcrack images under fluorescent excitation based on attention mechanism deep recurrent neural networks

This study aims to recognize fluorescent excitation images of microcracks in concrete components through the adoption of an Attention Mechanism-based Deep Recurrent Neural Network (RNN) model, thereby enhancing the accuracy and efficiency of crack detection. Considering the significance of concrete crack detection and the limitations in efficiency and accuracy of existing methods, this paper proposes an innovative image processing technique that combines fluorescent excitation methods with deep learning models to achieve earlier and more accurate identification of concrete microcracks. C30 concrete specimens were prepared experimentally and treated with fluorescent solution spray. Fluorescent images of cracks under UV light excitation were collected and processed using a segmented attention mechanism deep RNN model. Various performance evaluation metrics, including mean Intersection over Union (mIoU), mean Image Intersection over Union (miIoU), mean Image Dice Coefficient (miDice), and F1 score, were employed to comprehensively assess the model's performance. The results demonstrate that the proposed model achieved significant effectiveness in concrete crack image recognition, showing higher mIoU, miIoU, miDice, and F1 scores compared to other representative deep learning models, thus proving its advantages in recognition accuracy and efficiency. Particularly, by introducing the segmented attention mechanism, the model could capture microcrack features more effectively, significantly improving the accuracy of crack identification. This method not only provides a new technical approach for the early detection of concrete cracks but also lays the foundation for further development of efficient and accurate crack detection technologies to accommodate more complex engineering application scenarios.