# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 4 Experiments and Results
### Experimental Results and Analysis  
#### 4.4.1 Ablation Experiment  
The ablation experiment was performed on the NYU Depth-v2 Dataset from New York University to evaluate the effects of various enhancement strategies on the model. In the feature extraction phase of the algorithm, a feature extraction framework was developed utilizing the DNET backbone as a foundation. To assess its efficacy, we conducted a comparative analysis of the DNET backbone against the established ResNet50 [18] and DenseNet121 [17] backbones. Furthermore, the efficacy of the FPN module was corroborated. The experimental results are depicted in Figure 9.  
The experimental results suggest that the approach utilizing the DNET backbone outperforms Resnet50 and Densenet121 in terms of performance. In terms of RMSE metric, the DNET backbone demonstrates a prediction loss of 0.481, outperforming Resnet50 with 0.663, Densenet121 with 0.508, and DNET without FPN with 1.22. In the context of Abs Rel, the DNET backbone demonstrates a prediction loss of 0.192, surpassing the values of 0.294 for ResNet50, 0.228 for DenseNet121 and 0.352 for DNET without FPN. In terms of Sq Rel, the DNET backbone exhibits a prediction loss of 0.152, outperforming Resnet50 with 0.207, Densenet121 with 0.187 and DNET without FPN with 0.289. The exceptional performance can be credited to the innovative design of the DNET backbone in feature extraction, enabling the model to effectively capture nuanced and essential feature information across various levels, from low-level to high-level feature maps. The comparative outcomes of depth estimation between the proposed DNET backbone and the ResNet50 and DenseNet121 backbone are illustrated in Figure 10. In the near-field scenario of Scene 1, it is evident that the DNET backbone adeptly isolates the object situated at the lower-right corner of the table and captures the edge details of the chair, thereby producing a distinct outline of the object. In contrast, the ResNet50, DNET without FPN and DenseNet121 backbones suffer from the loss of pertinent information. In Scene 2, it is evident that the DNET backbone, characterized by dense convolutions, continues to exhibit significant feature extraction capabilities for remote scenes, thereby demonstrating a distinct superiority over the ResNet50, DNET without FPN and DenseNet121 backbones. The edge information of the distant bookshelf is accurately extracted, revealing a clear structure. In Scene 3, the DNET backbone successfully captures the internal structural information of  
Figure 9: DNET ablation experiment comparative result. Ablation experiments with FPN have been supplemented. the nearby chair, whereas the ResNet50, DNET without FPN and DenseNet121 backbones fail to retain this internal structural detail. To validate the effectiveness of the improved loss function Formula (4), based on the DNET backbone, ablation experiments were performed on the NYU Depth-v2 Dataset. The results are depicted in Figure 11.  
The improved loss function resulted in a decrease in the initial RMSE loss value from 0.443 to 0.408, indicating an 8% reduction. The accuracy metric \(\delta\) demonstrated an increase from 0.9094 to 0.9198, indicating a 3.4% enhancement. This enhancement can be credited to the refined loss function that tackles the imbalance in class distribution among the samples. It establishes a correlation between depth categories and continuous depth values, leading to varied penalty terms for distinct depth intervals despite having the same prediction deviation. This reduces the impact of distant scene samples on the training trajectory of the model, thus improving the predictive capacity of the network model in various depth scenarios. Figure 12 presents the results of the ablation study conducted before and after implementing the improved loss function. In Scene 4, it is apparent that the algorithm  
Figure 11: The ablation experiment results with the original and improved loss functions. Figure 10: Depth estimation results on NYU Depth-v2 Dataset using different backbones.