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Camellia oleifera orchard navigation presents considerable challenges, including the limited field of view of ground robots, discontinuous crop rows, and complex shadow interference. To address these challenges, this study proposes a UAV-based method for inter-row navigation line detection using an improved DSConv-YOLOv11 model. The Global Attention Mechanism is incorporated into the YOLOv11n backbone to enhance the model’s ability to focus on canopy features, while the original C3k2 module is replaced with C3k2_DySnakeConv to improve the extraction of geometric features associated with curved canopies and inter-row gaps. Navigation lines are generated through K-Means clustering of canopy center points, outlier removal, and cubic spline fitting. The improved model achieves a mean average precision (mAP) of 85.3%, representing a 5.2-percentage-point improvement over YOLOv11n, with an inference time of 303.33 ms per aerial image. The generated navigation lines achieve an average crop row detection accuracy (CRDA) exceeding 0.73 compared with manually annotated paths, with a mean heading deviation of 5.25° and a lateral deviation of 11.62 pixels. Comparative experiments using a C. oleifera picking robot further demonstrate that the extracted navigation paths are generally consistent with the robot’s actual driving trajectory and meet the requirements of inter-row operations. The proposed method provides a feasible technical solution for detecting inter-row navigation lines in C. oleifera orchards from a low-altitude remote-sensing perspective.