College of Information Science and Engineering, Xinjiang University, Urumqi 830049, China.
College of Mechanical Engineering, Guangxi University, Nanning 530004, China.
Sensors (Basel). 2022 Jun 22;22(13):4696. doi: 10.3390/s22134696.
Road detection is a crucial part of the autonomous driving system, and semantic segmentation is used as the default method for this kind of task. However, the descriptive categories of agroforestry are not directly definable and constrain the semantic segmentation-based method for road detection. This paper proposes a novel road detection approach to overcome the problem mentioned above. Specifically, a novel two-stage method for road detection in an agroforestry environment, namely ARDformer. First, a transformer-based hierarchical feature aggregation network is used for semantic segmentation. After the segmentation network generates the scene mask, the edge extraction algorithm extracts the trail's edge. It then calculates the periphery of the trail to surround the area where the trail and grass are located. The proposed method is tested on the public agroforestry dataset, and experimental results show that the intersection over union is approximately 0.82, which significantly outperforms the baseline. Moreover, ARDformer is also effective in a real agroforestry environment.
道路检测是自动驾驶系统的关键部分，语义分割被用作此类任务的默认方法。然而，农林复合系统的描述类别不可直接定义，这限制了基于语义分割的道路检测方法。本文提出了一种新的道路检测方法来克服上述问题。具体来说，提出了一种用于农林复合环境下道路检测的新型两阶段方法，即 ARDformer。首先，使用基于转换器的分层特征聚合网络进行语义分割。在分割网络生成场景掩模后，边缘提取算法提取轨迹的边缘。然后，计算轨迹和草所在区域的外围以包围该区域。在公共农林复合数据集上进行了实验，实验结果表明，交并比约为 0.82，明显优于基线。此外，ARDformer 在真实的农林复合环境中也很有效。
