## 4 Conclusions  
In this paper, a vison-based road recognition method based on an enhanced U-shaped IndoorPathNet and transfer learning is introduced, which is specifically designed for AMRs in large-scale flat warehouses. The efficiency and accuracy of road feature extraction is significantly improved, leveraging structural optimization, spatial self-attention mechanisms, dilated convolutions, feature transfer learning, edge detection auxiliary task and data augmentation. Compared with currently mainstream FCN-8s, FCN-16s, SegNet, and Unet, the proposed method exhibits exceptional performance on our proprietary WIPD, effectively providing real-time directional guidance for AMRs operating in large-scale dynamic warehouse environments. After extensive experimental trials, the model proposed in this paper achieves an average processing speed equivalent to approximately 9.81 frames per second (FPS) on a single 3060 GPU and an IOU of 98.4%. This performance fully satisfies the real-time global guidance needs of low-speed AMRs navigating within warehouse environments.  
Although the IndoorPathNet is proposed to realize the path recognition task, some problems and shortcomings still arise in widespread practical use. The presented case studies are not conducted in natural light environments, anticipating occurrences where floor areas illuminated by light spots may be erroneously identified as obstacles, along with false detections of walls. In addition, path information of the entire flat warehouse is difficult to obtain based on a single BEV vision, which means that the possibility of utilizing multiple surveillance cameras to segment paths from diverse perspectives presents a promising solution to address perspective deficiencies. It is essential to emphasize that the semantic map of the paths generated by our method lacks physical scale and necessitates the acquisition of physical information through other sensors to meet the navigation requirements of AMRs. As a potential solution, local environmental perception by the onboard sensors of AMRs can effectively compensate for the absence of obstructed paths in the BEV, and enable fully automated scanning and mapping, which forms an important part of the multi-modal SLAM framework.  
Figure 8: Training metrics on WIPD.  
**Author Contributions**: Data curation, T.Z. and S.L.; Formal analysis, T.Z., C.H. and S.L.; Funding acquisition, C.H.; Investigation, T.Z. and C.H.; Methodology, T.Z.; Project administration, L.Q.;Resources, Z.W. and L.Q.; Software, T.Z. and R.L.; Supervision, S.Z.; Validation, C.H.; Visualization, T.Z.; Writing--original draft, T.Z.; Writing--review and editing, C.H. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This paper was funded by National Natural Science Foundation of China (Grant Number: 52305285), Natural Science Foundation of Jiangsu Province (Grant Number: BK20230592), Open Fund Project of State Key Laboratory of Fluid Power and Mechatronic Systems (Grant Number: GZKF-202326), Innovative Science and Technology Platform Project of Cooperation between Yangzhou City and Yangzhou University, China (Grant Number: YZ2020266).  
**Data Availability Statement**: The data presented in this study are available on request from the first author, [PERSON]. The data are not publicly available due to privacy.  
**Conflicts of Interest**: Author [PERSON] was employed by the company Canny Elevator. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.