Automatic row guidance is important for efficient and low-loss corn harvesting. However, complex field conditions challenge reliable inter-row perception, while many deep learning models remain computationally demanding. To address this, a lightweight semantic segmentation network is proposed. A dataset covering challenging field conditions was constructed. Based on DeepLabV3+, GhostNetV2 was adopted to reduce computational cost, an SP-ASPP was designed to enhance the representation of elongated inter-row structures, and BiFormer was introduced to strengthen long-range contextual modeling. We jointly exploited directional multi-scale context and sparse long-range interactions to preserve continuous row-space structures under occlusion and background interference while maintaining a lightweight architecture. A composite loss combining Focal, Dice, and Boundary losses was further employed to improve region completeness and boundary localization. A navigation-line extraction algorithm was then developed to generate stable guidance paths. After structured pruning and TensorRT FP16 optimization, the model achieved an mIoU of 82.28% at 53.1 FPS on the edge platform. The extracted navigation line yielded a mean absolute lateral error of 4.2 cm and a mean absolute heading error of 2.17°. These results demonstrate that the proposed method provides accurate real-time navigation perception on resource-constrained hardware, supporting low-cost vision-based corn harvester guidance.