# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 1 Introduction
The summary of monocular depth estimation algorithms is shown in Table 1.  
To tackle the aforementioned issues, this study introduces a monocular depth estimation approach that relies on an end-to-end convolutional neural network. The methodology \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Main Contributions** & **Year of Submission** \\ \hline Eigen & Introduces multi-scale deep networks for monocular depth estimation, improving accuracy by capturing global and local depth information. & 2014 \\ \hline DCNF & Combines deep neural networks with Conditional Random Fields to significantly improve image segmentation and object recognition accuracy. & 2015 \\ \hline DORN & Introduces ordinal regression to depth estimation, leading to more precise and robust depth predictions in complex scenes. & 2018 \\ \hline BTS [16] & Enhances depth estimation accuracy by using a “big to small” architecture for progressive refinement. & 2019 \\ \hline DAV & Integrates a dynamic attention mechanism with a variational model to enhance depth estimation accuracy. & 2020 \\ \hline DDP & The model employs a generative paradigm via a conditional diffusion pipeline, gradually removing random Gaussian noise guided by the image in order to achieve precise and dense visual predictions. & 2023 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of monocular depth estimation algorithms. comprises three components: a feature extraction module, a feature fusion module, and a depth perception module. In the feature extraction stage, a Dense Convolutional Network (DNET) is employed for extracting features from the input image. This approach helps mitigate the issue of gradient vanishing and improves feature propagation. In the feature fusion stage, dilated convolutions are employed to extract and integrate feature maps with varying receptive fields. This augmentation enhances the effective receptive field of the convolution kernels without escalating the parameter count, enabling the network to encompass a more extensive contextual semantic information. During the depth estimation stage, the monocular depth estimation issue is converted into an ordinal multi-class classification problem. The method involves designing a model tailored for ordinal multi-class problems, which entails mapping input data to depth data. In the meantime, a loss function optimization algorithm targeting class imbalance was proposed, which led to an improvement in the model's prediction accuracy. The paper presents several key contributions as follows:  
(1) A novel convolutional neural network (CNN) model, designated DNET, is proposed. This model exhibits several advantageous characteristics, including dense connectivity, effective gradient propagation, efficient parameter sharing, and the suppression of feature sparsity. (2) A feature fusion module is introduced, which is based on dilated convolution. This module enhances the effective receptive field of convolutional kernels without increasing the network parameters, thereby enabling the extraction of more comprehensive contextual semantic information. (3) An optimization algorithm designed for addressing class imbalance in the loss function establishes a correlation between depth categories and continuous depth, thereby enhancing the accuracy of depth estimation within the network model.