Crop row detection has garnered significant interest due to its critical role in enabling navigation in GPS-denied environments, such as under-canopy agricultural settings. To address this challenge, we propose RowDetr, an end-to-end neural network that utilizes smooth polynomial functions to delineate crop boundaries in image space. A novel energy-based loss function, PolyOptLoss, is introduced to enhance learning robustness, even with noisy labels. The proposed model demonstrates a 3% improvement over Agronav in key performance metrics while being six times faster, making it well-suited for real-time applications. Additionally, metrics from lane detection studies were adapted to comprehensively evaluate the system, showcasing its accuracy and adaptability in various scenarios.

翻译：作物行检测因其在GPS拒止环境（如冠层下农业场景）中实现导航的关键作用而受到广泛关注。为解决这一挑战，我们提出RowDetr——一种利用平滑多项式函数在图像空间中描绘作物边界的端到端神经网络。我们引入了一种基于能量的新型损失函数PolyOptLoss，即使在标签存在噪声的情况下也能增强学习鲁棒性。所提模型在关键性能指标上较Agronav提升3%，同时速度提升六倍，非常适合实时应用。此外，我们借鉴车道检测研究的评价指标对系统进行全面评估，展示了其在多种场景下的准确性与适应性。