Enhanced Monocular Depth Estimation Based on Improved Self-Attention Mechanisms and Composite Loss Functions

Monocular depth estimation, as a key technology in computer vision, has been widely applied in autonomous driving, robotic navigation, and other fields. However, balancing the accuracy of global depth prediction with the fine recovery of local details remains challenging. This paper presents an improved monocular depth estimation framework based on the powerful Depth Anything V2 pre-trained model. First, we fine-tune the ViT-Large pre-trained weights and integrate Query-Key Normalization (QK-Norm), Relative Position Bias (RelPosBias), and Rotary Position Embedding (RoPE) mechanisms into its core self-attention module. These mechanisms enhance the model's ability to perceive spatial information and relative positional relationships, improving the stability and position sensitivity of feature representation. Second, to address common issues of local discontinuities and edge blurring in depth prediction, we construct a composite loss function consisting of improved Scale-invariant Logarithmic Loss (SiLogLoss), Gradient Consistency Loss (GradientLoss), and Edge Smoothness Loss (EdgeSmoothnessLoss), providing supervision from global error, depth map structure, and edge details. We conducted comprehensive experimental evaluations on the KITTI dataset. Results demonstrate that our method achieves significant improvements over the original Depth Anything V2 model in key performance metrics: reducing the Absolute Relative Error (AbsRel) from 0.074 to 0.064, and increasing the $\delta_1$ accuracy from 0.946 to 0.967, validating the effectiveness of our proposed strategies.