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The interrow path of maize in the middle and late stages is interfered by factors such as insufficient light and occlusion, which is not favorable to the detection of navigation lines during autonomous operation of agricultural robots. To address this problem, an algorithm based on the improved Fast-SCNN semantic segmentation model for detecting the navigation lines in the interrow path of maize in the mid-late stage was proposed. Firstly, to address the problem that the current path semantic segmentation model was not accurate enough for edge segmentation in the mid-late maize environment, an Edge-FastSCNN model was proposed, and the edge extraction module (EEM) proposed was introduced in the model branch to obtain accurate path boundary information, and spatial pyramid pooling was introduced into the model. Atrous spatial pyramid pooling (ASPP) module was introduced in the model to fuse the image boundary information and deep features. Then based on the interline path mask predicted by the model, the left and right boundary points of the path mask were detected by pixel scanning method, and the midpoint of the path mask was obtained by weighted average method. Finally, the least squares method was used to fit the navigation lines to achieve the detection of the mid- and late-stage maize interline path navigation lines. In order to verify the performance of the proposed method, model performance comparison experiments and navigation line detection experiments were conducted based on five environments such as normal light without shade, insufficient light, shadows, weeds shade, and leaf shade of maize in the middle and late stages. The experimental results showed that the average intersection and merger ratio of the model was 97.90%, the average pixel accuracy was 98.84%, the accuracy rate was 99.39%, and the inference speed was 63.0 f/s;the average intersection and merger ratio of the model in the five environments mentioned above was ranged from 96.93% to 98.01%, and the average pixel accuracy was ranged from 98.33% to 99.03%, and the accuracy rate was from 98.53% to 99.12%;the average value of heading angle deviation between the predicted navigation line and the real navigation line in the above five environments was 1.15°~3.16°, and the average pixel lateral distance was 1.89~ 3.41 pixels;the average processing time for a single-frame image of the navigation line detection algorithm was 90.04 ms. Therefore, the navigation line detection algorithm proposed met the mid- and late-stage maize interline path navigation task’s accuracy and real-time requirements.