# Monocular Vision-Based Depth Estimation of Forward-Looking Scenes for Mobile Platforms
## 1 Introduction
Depending on the training sample, depth estimation methods can be categorized into monocular and binocular systems [16]. In a binocular system, provided that the left and right cameras capture identical scenes, both cameras must acquire images simultaneously as training samples. The depth information of the scene can then be inferred through the parallax between the left and right views. However, this approach inevitably increases both computational complexity and hardware costs, making it less optimal for certain IMPs. Additionally, some IMPs, such as UAVs, are relatively small in size, which restricts the baseline length between the binocular cameras and limits the range of depth measurement. In contrast, the monocular system is not constrained by baseline length; the neural network only needs to compare differences between multiple frames captured by a moving monocular camera to obtain depth information, making it more suitable for IMPs with high maneuverability. Monocular depth estimation methods based on deep learning can be categorized into three types: supervised, unsupervised (self-supervised), and semi-supervised learning [17; 18]. Notable examples of supervised and semi-supervised approaches include the coarse-to-fine method [19], the deep convolutional neural field model [20], and Generative Adversarial Networks (GANs) [21]. Both supervised and semi-supervised methods require the integration of pixel-level ground truth depth during model training, which necessitates high-precision instruments for accurate ground truth data acquisition. Furthermore, for semi-supervised approaches, the use of high-precision instruments such as LIDAR can lead to significant cost increases. These limitations present challenges in applying these methods to certain IMPs. In contrast, depth estimation methods based on self-supervised learning combine deep learning with geometric vision, allowing for the extraction of supervised information and the construction of data labels from inherent geometric relationships without the need for ground truth data. This approach circumvents the issues associated with supervised and semi-supervised learning methods. Consequently, the method presented in this paper falls within the more cost-effective category of self-supervised learning methods. In the realm of self-supervised monocular depth estimation, _Monodepth2_ stands out as a representative and exceptional algorithm, providing an end-to-end solution for depth estimation that does not rely on additional sensors or preprocessing steps [22]. The fundamental framework of _Monodepth2_ is based on a U-Net architecture with an encoder-decoder structure. The encoder extracts detailed and semantic features from the input image, while the decoder transforms these features into pixel-level depth values. However, the lack of a multi-layer feature fusion mechanism between the encoder and decoder in _Monodepth2_, combined with its simplistic network architecture, leads to the loss of critical semantic and detailed information. Experimental results across various datasets indicate that the depth maps generated by _Monodepth2_ exhibit blurring and lack detail in local regions with significant gradients. Moreover, its predictive performance is further diminished under low-light conditions, making it challenging to maintain robustness across diverse lighting scenarios. It is well known that IMPs are commonly employed in various operational environments, particularly when faced with poor conditions. Consequently, _Monodepth2_ may produce substantial depth estimation errors, making it difficult to apply directly to IMPs [23]. This paper proposes a novel self-supervised monocular depth estimation method that enhances _Monodepth2_, with the contributions summarized as follows:  
1. An intermediate module that incorporates multi-layer feature aggregation nodes is proposed to further integrate feature maps with varying resolutions.