## 5 Conclusions and Future Work  
In summary, in order to solve the problem of inaccurate lane segmentation of indoor AGV in an overexposure environment, a lane detection method combining image inpainting and segmentation is proposed. In the proposed method, the overexposed lane image is repaired and reconstructed by the MAE network, and then input into the image segmentation network for lane segmentation and extraction. The optimal MAE parameters suitable for the proposed method are obtained by experiments. The reduction in the MAE network model parameters reduces the inference time of the proposed method in the overexposed environment. An image segmentation network CSTA is proposed, which uses a lightweight backbone network to improve inference speed. Moreover, the triple attention module is used to improve the segmentation quality, so as to obtain clearer lane contours. It is especially obvious when the lanes are narrow. Meanwhile, the proposed method has better noise suppression. The effect and quality of the segmentation of the network are significantly improved. Finally, the efficiency of the proposed lane extraction method is verified by three image segmentation evaluation metrics (IoU, F\({}_{1}\)-score, and PA) and inference time in the case of overexposure and proper exposure. Experimental results show that the proposed lane extraction method based on image inpainting and image segmentation has excellent segmentation performance and fast inference speed in an overexposure environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Module Composition**} & \multicolumn{3}{c}{**Performance Evaluation Metrics**} \\ \hline \multirow{2}{*}{**AG**} & **Spatial** & **Channel** & \multirow{2}{*}{**IoU**} & \multirow{2}{*}{**F\({}_{1}\)-Score**} & \multirow{2}{*}{**PA**} \\  & **Attention** & **Attention** & & & \\ \hline \(\times\) & \(\times\) & \(\times\) & 0.7949 & 0.8806 & 0.9861 \\ \(\surd\) & \(\times\) & \(\times\) & 0.8114 & 0.8905 & 0.9873 \\ \(\times\) & \(\surd\) & \(\times\) & 0.8197 & 0.8954 & 0.9881 \\ \(\times\) & \(\times\) & \(\surd\) & 0.8073 & 0.8881 & 0.9867 \\ \(\times\) & \(\surd\) & \(\surd\) & 0.8001 & 0.8841 & 0.9858 \\ \(\surd\) & \(\surd\) & \(\surd\) & 0.8293 & 0.9031 & 0.9885 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Segmentation evaluation results for different combinations of attention modules.  
However, the proposed method has limitations. The experimental scenes of the proposed method are all set indoors, the lane lines used for collecting datasets are also clear and clean. The experiment was not carried out on the ground with debris and dirt. Therefore, in the future it is necessary to further study the treatment scheme of outdoor AGV lane overexposure or lane line defects and stains to further improve the practical application scope of this research. In the future, we will strive to achieve this goal in a more efficient manner.  
X.Y.: Conceptualization, Methodology, Writing--original draft. Z.Y.: Data curation, Software, Writing--original draft. J.H.: Supervision, Project administration. L.W.: Investigation, Conceptualization. B.Z.: Investigation, Conceptualization. Y.Z.: Resources, Investigation. J.Z.: Resources, Investigation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
We are very grateful to the support from [PERSON].  
The authors declare no conflict of interest.