用多项式直接参数化作物行，实现高精度实时检测。
RowDetr: End-to-End Crop Row Detection Using Polynomials
- 用多项式表示作物行，避免后处理，提升效率。
- 在6962张图像上达F1 0.74，位置偏差低至0.405米。
- 支持边缘设备部署，推理延迟仅3.5毫秒（量化后）
作物行检测使自主机器人能在无GPS环境下导航。基于视觉的方法常因行间空隙、弯曲或遮挡而失效，且地表以下环境标注困难。本文提出RowDetr，一种基于Transformer的轻量级端到端神经网络，用于精准农业中的作物行检测。该模型采用混合编码器，结合新型多项式表示法，可直接参数化直线、曲线及遮挡的作物行，无需复杂后处理。核心创新包括PolySampler模块与多尺度可变形注意力，配合能量型损失函数PolyOptLoss，优化几何对齐并增强对标注噪声的鲁棒性。在包含6,962张高分辨率图像的多样化数据集上评估，覆盖多种作物类型，结果表明其性能优于AgroNav与RolColAttention等先进方法，最高F1达0.74，车道位置偏差低至0.405米。系统实现实时推理，延迟为6.7毫秒，经INT8量化后降至3.5毫秒，在NVIDIA Jetson Orin AGX上表现优异。本研究凸显多项式参数化的高效性，特别适合部署于农业机器人和自动驾驶农机的边缘计算设备。
原文摘要 · Abstract (English)
Crop row detection enables autonomous robots to navigate in gps denied environments. Vision based strategies often struggle in the environments due to gaps, curved crop rows and require post-processing steps. Furthermore, labeling crop rows in under the canopy environments accurately is very difficult due to occlusions. This study introduces RowDetr, an efficient end-to-end transformer-based neural network for crop row detection in precision agriculture. RowDetr leverages a lightweight backbone and a hybrid encoder to model straight, curved, or occluded crop rows with high precision. Central to the architecture is a novel polynomial representation that enables direct parameterization of crop rows, eliminating computationally expensive post-processing. Key innovations include a PolySampler module and multi-scale deformable attention, which work together with PolyOptLoss, an energy-based loss function designed to optimize geometric alignment between predicted and the annotated crop rows, while also enhancing robustness against labeling noise. RowDetr was evaluated against other state-of-the-art end-to-end crop row detection methods like AgroNav and RolColAttention on a diverse dataset of 6,962 high-resolution images, used for training, validation, and testing across multiple crop types with annotated crop rows. The system demonstrated superior performance, achieved an F1 score up to 0.74 and a lane position deviation as low as 0.405. Furthermore, RowDetr achieves a real-time inference latency of 6.7ms, which was optimized to 3.5ms with INT8 quantization on an NVIDIA Jetson Orin AGX. This work highlighted the critical efficiency of polynomial parameterization, making RowDetr particularly suitable for deployment on edge computing devices in agricultural robotics and autonomous farming equipment. Index terms > Crop Row Detection, Under Canopy Navigation, Transformers, RT-DETR, RT-DETRv2
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