Intelligent identification of tunnel water leakage based on super-resolution reconstruction and triple attention

To effectively solve the issues of poor anti-environmental interference and inaccurate defect boundary segmentation in tunnel water leakage intelligent identification methods, an intelligent segmentation method based on super-resolution reconstruction and triple attention is proposed in this paper, named TR-Unet. In TR-Unet, the Real-ERSGAN super-resolution reconstruction algorithm is used to perform resolution enhancement on water leakage images to strengthen the edge details of the defect regions. Then triple attention mechanism is established by fusing channel attention, spatial attention, and self-attention, and is introduced into the encoder of the Unet network, thus enhancing the model's resistance to environmental interference. The results show that the mIoU and mPA of the TR-Unet are 84.31% and 88.95%, respectively, which are higher than the other compared models. In addition, TR-Unet has better segmentation effect of water leakage, which mainly reflected in less false recognition and better edge details.