## 1 Introduction
Early researchers exploring computer vision for mobile robot navigation proposed methods like a One-Versus-Rest (OVR)-type Support Vector Machine (SVM) classifier with relaxed constraints and kernel functions [16]. This approach integrated illumination-aware image segmentation, and guided color enhancement and adaptive threshold segmentation to tackle path recognition challenges in complex lighting conditions. By employing particle filters, researchers have successfully navigated the fusion of data from conventional one-dimensional image databases and directed graphs. This approach facilitates precise location identification within networked environments, including urban streets and multi-lane roads where lateral platform shifts occur [17]. As instance segmentation techniques represented by neural networks have gained prominence [18], road recognition has evolved from basic filter-based region recognition to pixel-level semantic segmentation relying on Convolutional Neural Networks (CNNs). The rapid development of deep learning techniques, notably CNNs, has greatly advanced the effectiveness and efficiency of road image recognition. Semantic segmentation technology enhances the precision of road recognition by effectively distinguishing road areas from other objects, thereby improving the accuracy of road extraction and topological analysis. The advent of Fully Convolutional Networks (FCNs) [19] expanded image classification to the pixel level, mitigating the image reduction issue induced by pooling while enriching semantic information in features. However, traditional FCN approaches often overlook spatial pixel relationships, yielding fragmented segmentation outcomes [20]. Some researchers have utilized ResNet-18 with onboard cameras to differentiate between ground and walls [21]. The U-Net architecture, initially tailored for medical image segmentation, adopts a distinctive \"U\"-shaped configuration, with the encoder progressively reducing spatial dimensions and the decoder systematically restoring details and spatial attributes [22]. This unique design fortifies feature propagation and utilization efficacy, thereby enhancing segmentation precision and versatility. The encoder's hierarchical arrangement ensures the extraction of semantic insights across multiple scales, facilitating comprehensive interpretation of image content [23]. [PERSON] et al. proposed a road intersection recognition method that combines a classification model based on GPS data with clustering algorithms [24]. They also introduced a technique utilizing real-time video captured by drones or pre-downloaded satellite multispectral images for road extraction and road condition detection [25]. [PERSON] et al. proposed a multi-path semantic segmentation network incorporating convolutional attention guidance to assist vehicles in road recognition [26]. Transfer learning provides an efficient manner of learning and transferring knowledge between previous tasks and the target task when the target data are rare, inaccessible, expensive and difficult to compile [27]. Transfer learning often involves three main strategies: direct transfer, feature extraction, and fine-tuning [28]. Direct transfer preserves the pre-trained model's convolutional layers, replacing only the fully connected layers for the new task. Feature extraction uses the pre-trained model's convolutional layers to extract feature vectors, which are then input into a new fully connected network. Fine-tuning adjusts selected convolutional layer parameters to better fit the new task, often freezing certain layers or training custom, fully connected layers for improved performance. [PERSON] et al. [29] developed a Deep Q-Network (DQN) Lyapunov-based method for nonconvex Urban Aerial Vehicle (UAV) path design in various simulations and introduced a transfer learning strategy to enhance learning in new mmWave domains using knowledge from a sub-6 GHz domain. [PERSON] et al.