To address the challenges of inadequate feature fusion

loss of fine-grained details

background noise interference

and suboptimal computational efficiency in existing rock fracture identification methods

the present study proposes a fracture recognition approach based on the triple feature efficient-fusion U²-Net(TFE-U²-Net) model. This approach is integrated with an image preprocessing algorithm based on gray-based fracture region preselection(GFRP) to significantly enhance the processing efficiency for large-scale

high-resolution images. The proposed improvements include: (1) integrating a triple feature fusion (TFF) mechanism into the U²-Net framework to strengthen multi-scale feature extraction; (2) embedding an efficient channel attention (ECA) module to suppress background noise and enhance feature representation; and (3) replacing standard convolutions with depthwise separable convolutions to significantly reduce model complexity. Based on a self-constructed rock fracture image dataset

ablation studies and comparative experiments were conducted to validate the model’s performance. Experimental results show that the proposed method reduces the model parameter count by 39.2% compared to the original U²-Net

while achieving IoU and scores of 81.07% and 89.55%

respectively—significantly outperforming baseline models. The TFF mechanism effectively mitigates detail loss

and the ECA module enhances the model's robustness under noisy conditions.