## 1 Introduction
[30] proposed a deep reinforcement learning algorithm that helps models achieve navigation capabilities between cities without maps, realizing the transfer learning of task content. Given the scarcity of semantic data tailored for warehousing tasks in public datasets, this paper employs transfer learning to address the challenge of insufficient training samples and features. However, traditional and deep-learning-based road recognition methods currently face significant challenges, including the misidentification of road surfaces and backgrounds. Environments with complex obstructions--such as buildings, trees, pedestrians, and vehicles--often lead to substantial misdetection issues. Additionally, roads that are narrow or short in length are prone to being overlooked, adversely affecting the overall efficiency, accuracy, and stability of these systems. Compounding these issues is the scarcity and high cost of high-quality datasets for large-scale indoor warehousing environments, which hampers the smooth implementation of neural-network-based road recognition. [PERSON] et al. [31] surveyed VSLAM methods and found common issues such as efficiency degradation, unstable camera motion, and occlusions. These challenges are particularly prominent in scenarios involving moving objects, loop closure, robot kidnapping, and large-scale mapping. [PERSON] et al. [32] assessed and contrasted different real-time RGB-D visual odometry algorithms tailored for mobile devices, highlighting that only premium devices such as the iPad Air could uphold a satisfactory frame rate at a standard Video Graphics Array resolution. Thus, there is still a need to improve the road recognition method to avoid the above problem. This paper proposes an IndoorPathNet path extraction scheme based on BEV images sourced from warehouse surveillance, aiming to tackle the challenge of intelligent road recognition for AMRs in vast and dynamic warehouse settings. The main contributions of the paper can be summarized as follows:  
1. The proposed IndoorPathNet mitigates overfitting through structural optimization, and enhances its capacity to handle crucial information by integrating spatial attention mechanisms and dilated convolutions to widen the receptive field, thereby improving the efficiency and completeness of path feature extraction;
2. Pre-training implemented on the UAVID dataset facilitates the rapid learning of road features, augmenting its versatility and convergence speed;
3. To enhance generalization capability and training efficiency, this paper incorporates edge detection as an auxiliary task and employs random image cropping and stitching for data augmentation, thereby substantially enlarging the training set and enhancing image diversity. The remaining sections of the paper are structured as follows: Section 2 provides a detailed exposition of IndoorPathNet's structural optimization, spatial self-attention mechanism, dataset, and transfer learning strategy; Section 3 showcases and discusses the comparative experiments between IndoorPathNet and four other models, along with the ablation experiments conducted on the Warehouse Indoor Path Dataset (WIPD); and finally, Section 4 provides conclusions and the future directions, respectively.