## 3 Proposed Method
Various image augmentation techniques are applied during training, including random horizontal flipping, scaling within the range of [0.125, 1.5], and cropping to a size of 1024 \(\times\) 512, which are typical strategies for segmentation tasks [22]. Additionally, the RandAug [39] augmentation set is employed to adjust brightness, contrast, and saturation. The cross-entropy loss is calculated with a batch size of 16, acknowledging the importance of batch size in model performance. In DriveSegNet, weights pre-trained on the ImageNet [40] dataset are leveraged, while other modules are initialized with PyTorch's default weights [41]. To boost convergence speed without sacrificing model accuracy, mixed-precision training is adopted. On CamVid, the training settings are similar to those on Cityscapes. Random horizontal flipping, random scaling within the range of [0.5, 2.5], and random cropping to 720 \(\times\) 960 are applied, with a batch size of 24. The initial learning rate is set to 0.01, the weight decay rate is 0.0001, and the number of iterations is 1000. We set the learning rate to 0.0003 and the weight decay factor to 0.05 for the ADE20K dataset, with the learning rate of the backbone network scaled by a factor of 0.1. A batch size of 32 was used, with a fixed crop size of 512 \(\times\) 512. All inference experiments were conducted on an RTX 3090 under the environment of PyTorch 1.8.1, Python 3.8 (Ubuntu 18.04), Cuda 11.1, and TensorRT 10.6.0. We utilized FPS (frames per second) to assess inference speed and applied conventional performance assessment metrics tailored specifically for the semantic segmentation domain and the specific research goals. In this study, we primarily use the mean Intersection over Union (mIoU) as the evaluation metric because it provides a comprehensive measure of both segmentation accuracy and boundary quality. Unlike accuracy, mIoU effectively avoids the potential bias in class-imbalanced datasets, offering a more accurate assessment of the model's performance across all classes. The calculation method for IoU [42] is detailed in Equation (10) as follows:

\[\text{IoU}\ =\ \frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{10}\]

The detailed calculation method for mIoU [3] is outlined in Equation (11) as follows:

\[\text{mIoU}\ =\ \frac{1}{\text{k}+1}\sum_{\text{i=0}}^{\text{k}}\text{IoU}_{\text {K}} \tag{11}\]

where FN represents the number of samples incorrectly predicted as negative, FP represents the number of samples incorrectly predicted as positive, and TP refers to the number of samples that are actually positive and correctly predicted as positive. Additionally, we report frames per second (FPS) as a key metric for evaluating real-time processing capability. FPS is crucial in real-time applications such as autonomous driving, reflecting the model's inference speed and practical applicability. The FPS evaluation for our DriveSegNet model follows the methodology outlined by [PERSON] et al. [16]. We first performed multiple warm-up iterations on a single RTX 3090 GPU to stabilize the hardware performance. Subsequently, the model was run for N consecutive forward passes with a fixed input image size. The total execution time, T, was recorded, and the average inference time per image, t = T/N, was computed. From this, the frames per second (FPS) metric, which reflects the model's real-time processing capability, was calculated. In our experiments, we also provide three evaluation metrics--Precision, Recall, and F1 Score--on the Cityscapes and CamVid datasets to offer a comprehensive assessment of segmentation performance. Each of these metrics provides a unique perspective on segmentation accuracy, and their combined use facilitates a more thorough evaluation. ### Ablation Study

We conducted ablation experiments to validate the effectiveness of the proposed modules. The baseline model is DriveSegNet, which does not include the proposed modules. The training and inference settings for both datasets were consistent with those used in the comparison experiments.