Detection of weeds is essential to implement an intelligent weed control system in natural corn fields. Then, to address this issue, the Swin-UNet, Segmenter, and SegFormer deep learning transformer architectures have been implemented and compared. Furthermore, a simple thresholding method has been performed to enhance the segmentation. Moreover, a large pixel-level annotated image dataset acquired under natural field conditions is introduced to train the models. In addition, the well-known Precision, Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and mean Intersection over Union (mIoU) metrics have been used to evaluate the implemented models’ performance. According to the experimental results, the SegFormer architecture was the best model on each of the three proposed weed detection approaches, achieving a macro performance of up to 94.49%, 95.30%, and 91.26% for Precision, DSC, and mIoU, respectively.

中文翻译：

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使用深度学习 Transformer 架构增强特定于站点的杂草检测


杂草检测对于在天然玉米田中实施智能杂草控制系统至关重要。然后，为了解决这个问题，我们实现了 Swin-UNet、Segmenter 和 SegFormer 深度学习 transformer 架构并进行了比较。此外，还执行了一种简单的阈值方法来增强分割。此外，引入了在自然场条件下获取的大型像素级注释图像数据集来训练模型。此外，著名的精度、骰子相似系数 （DSC）、交交并比 （IoU） 和平均交交比 （mIoU） 指标已用于评估已实施模型的性能。根据实验结果，SegFormer 架构是所提出的三种杂草检测方法中的最佳模型，Precision 、 DSC 和 mIoU 的宏观性能分别高达 94.49%、95.30% 和 91.26%。