## 3 Proposed Method
Finally, the weights are processed again by a sigmoid activation function to prevent excessively large values, thereby avoiding extreme values that could destabilize the learning process. The computation process is described in Equation (9) as follows:

\[ \begin{gathered}\text{W}_{1}=\text{\sigma}(\text{f}_{5}\text{ W}_{4}+\text{b}_{4})\\ \text{W}_{2}=\text{\sigma}(\text{f}_{5}\text{ W}_{5}+\text{b}_{5})\\ \text{f}6=\text{f}_{1}*\text{W}_{1}+\text{f}_{2}*\text{W}_{2}=\text {f}_{1}/1+e^{-f_{5}\text{W}_{4}-\text{b}_{4}}+\text{f}_{2}/1+e^{-f_{5}\text{ W}_{5}-\text{b}_{5}}\end{gathered}  \tag{9}\]

### Feature Propagation Link (FPL)

Skip connections are a key component in deep neural network architectures. They are typically implemented through addition, where the input is added to the output. Their primary function is to mitigate the vanishing gradient problem and effectively accelerate the training process. In this network, we introduce three skip connections as shown in Figure 3d-f. These skip connections are designed to facilitate feature flow and information propagation by adding the convolved feature maps to the original feature maps or by fusing features from different stages. Specifically, in the processing of Stages 4 and 5, the convolved feature maps are added to the original feature maps, which helps preserve low-level feature information while enhancing the learning capability of high-level features. In the final fusion module, LAFM, features from different stages are fused by the module and then added to the original feature map, assisting the model in better integrating multi-level feature information. These skip connections help maintain feature diversity and strengthen the model's ability to learn from different scales and levels of information. ## 4 Experiments

In this section, we first introduce the dataset used in this study and provide a detailed description of the inference setup. Through ablation experiments, we analyze the impact of key modules on the model's performance. With quantitative analysis, we conduct a comprehensive comparison of DriveSegNet with state-of-the-art methods, highlighting its advantages in terms of generalization ability and practical application. ### Dataset

#### 4.1.1 Cityscapes

This dataset, comprising 5000 high-resolution street view images (1024 \(\times\) 2048 pixels), is used to evaluate semantic segmentation models. It covers various urban environments and is manually annotated with 19 categories. The dataset is split into training (2975), validation (500), and test (1525) subsets for model evaluation. #### 4.1.2 Camvid

The CamVid dataset, derived from images captured in Cambridge, UK, is widely used for urban traffic scene segmentation. It consists of about 500 images (720 \(\times\) 960 pixels) annotated with 11 categories, such as roads, buildings, and vehicles. The dataset is divided into training (367), validation (101), and test (233) subsets for model evaluation. #### 4.1.3 ADE20k

The ADE20K dataset comprises a total of 25,574 fully annotated images, along with an additional 2000 images that are also fully annotated. It encompasses a wide range of diverse scenes and a rich variety of object categories. The images in the dataset vary in size and include 150 distinct categories, which are further divided into 100 thing categories and 50 stuff categories. ### Experimental Details and Evaluation

For training on the Cityscapes dataset, Stochastic Gradient Descent (SGD) is chosen with a momentum coefficient of 0.9, an initial learning rate of 0.01, and a weight decay of 0.0005, aiming to accelerate early-stage convergence while controlling oscillations during optimization. A \"multi\" learning rate scheduler is used, employing a linear warm-up strategy from 0.1\(\times\) to the desired learning rate over the first 3000 iterations.