<p>Self-supervised monocular depth estimation that does not require ground truth for training has attracted attention in recent years. However, existing approaches still face challenges such as inaccurate predictions and blurred edges when handling regions with abrupt structural changes, including object boundaries and occluded areas. To address these problems, we propose a structure-aware guided self-supervised monocular depth estimation framework, focusing on improving depth prediction accuracy in edge regions. This method comprises three key modules: the Edge-Aware Feature Optimizer (EAFO), which predicts edge probability maps from high-level semantics of the encoder and guides feature enhancement in intermediate layers to strengthen boundary representations; the Adaptive Feature Enhancement Module (AFEM), which aggregates local and global semantic information using a dilated attention mechanism to maintain consistency in edge regions; and the Wavelet Domain Decoder Block (WBD), which introduces frequency-domain modeling during the decoding phase, achieving clear and structurally complete depth recovery through fusion of low-and high-frequency structural differences and attention guidance.Experimental results demonstrate that the proposed method achieves superior performance on datasets such as KITTI and Make3D, particularly outperforming existing mainstream self-supervised methods in edge structure regions. Meanwhile, it maintains low computational cost and parameter count, exhibiting excellent generalization ability and deployment potential.</p>