## 1 Introduction  
Currently, AGVs are widely used in various industries such as automated production, modern logistics warehousing, and transportation [1, 2, 3]. The guidance methods of AGV include electromagnetic navigation, magnetic stripe navigation, laser navigation, and visual navigation [4, 5]. Among them, electromagnetic navigation needs to embed the line into the ground in advance, which has high maintenance cost and is difficult to combine with other navigation methods for intelligent navigation. Magnetic stripe navigation has a similar problem. The equipment of laser navigation has a high manufacturing cost. Laser navigation is sensitive to external light, and has high working environment requirements. Visual navigation takes into account both low cost and high-precision navigation. Visual navigation is divided into traditional computer visual navigation and deep learning-based visual navigation. Deep learning has been widely used in many fields, such as industry, intelligent manufacturing, medical imaging, and automotive [6, 7, 8, 9]. In this context, deep learning has become one of the important components of AGV navigation [10]. The deep learning-based lane detection is usually formulated as the image segmentation problem [11]. That is, each pixel in the result of segmentation contains the label of its object class for each input. The lane detection based on deep learning image segmentation has been widely used in the field of unmanned vehicle driving. However, in the field of AGV vision line navigation, the deep learning-based image segmentation is not widely used. At present, most of the AGV lane detection methods are based on traditional computer vision lane segmentation and extraction algorithms [12; 13; 14], such as improved Hough transform [15] and improved Canny algorithm [16]. Although these traditional vision-based lane segmentation and extraction algorithms have made good progress, there is a large gap in real-time performance, accuracy and robustness compared with deep learning methods. In addition, the requirements of segmentation accuracy and inference speed cannot be met simultaneously by existing image segmentation networks. These networks, such as ERFNet [17], improve the inference speed, but their segmentation accuracy is not satisfactory. PP-LiteSeg [18] and FLANet [19] have good segmentation accuracy, but their real-time performance is poor. Good memory resource-saving ability is possessed by CCNet [20], but its real-time performance is poor. CSTA segmentation network is proposed in this paper, which has fast segmentation speed on the premise of satisfying segmentation accuracy, and is suitable for a real-time AGV visual navigation system. However, when the image collected by AGV has overexposed areas in the lane, all the existing image segmentation methods cannot extract the lane completely. Aiming at the problem of image overexposure in AGV lane detection, recent research mainly focuses on image filtering algorithms and threshold adjustment algorithms [21; 22]. When the overexposure degree is too heavy, the existing algorithms cannot reconstruct the lane image. However, the proposed method includes first inpainting the lane image based on deep learning, and then performs image segmentation. By this way to extract the lane trajectory, the proposed method achieved high accuracy in the experiment. The main objective of the work in this paper are as follows:  
1. An AGV lane extraction method combining image inpainting and image segmentation network is proposed to improve the accuracy of AGV lane segmentation in overexposure conditions;
2. The network parameters of MAE are optimized, reduced, and verified by experiments. On the premise of ensuring the repair quality, the inference speed of MAE model is improved;
3. A convolutional skip triple attention network (CSTA) is designed. It meets the requirements of segmentation accuracy and segmentation speed at the same time.