基于 YOLOX 的稻田苗期杂草目标检测。

Weed target detection at seedling stage in paddy fields based on YOLOX.

机构信息

College of Electronic Information Engineering, Guangdong University of Petrochemical Technology, Maoming, China.

College of Engineering, South China Agricultural University, Guangzhou, China.

出版信息

PLoS One. 2023 Dec 13;18(12):e0294709. doi: 10.1371/journal.pone.0294709. eCollection 2023.

Weeds are one of the greatest threats to the growth of rice, and the loss of crops is greater in the early stage of rice growth. Traditional large-area spraying cannot selectively spray weeds and can easily cause herbicide waste and environmental pollution. To realize the transformation from large-area spraying to precision spraying in rice fields, it is necessary to quickly and efficiently detect the distribution of weeds. Benefiting from the rapid development of vision technology and deep learning, this study applies a computer vision method based on deep-learning-driven rice field weed target detection. To address the need to identify small dense targets at the rice seedling stage in paddy fields, this study propose a method for weed target detection based on YOLOX, which is composed of a CSPDarknet backbone network, a feature pyramid network (FPN) enhanced feature extraction network and a YOLO Head detector. The CSPDarknet backbone network extracts feature layers with dimensions of 80 pixels ⊆ 80 pixels, 40 pixels ⊆ 40 pixels and 20 pixels ⊆ 20 pixels. The FPN fuses the features from these three scales, and YOLO Head realizes the regression of the object classification and prediction boxes. In performance comparisons of different models, including YOLOv3, YOLOv4-tiny, YOLOv5-s, SSD and several models of the YOLOX series, namely, YOLOX-s, YOLOX-m, YOLOX-nano, and YOLOX-tiny, the results show that the YOLOX-tiny model performs best. The mAP, F1, and recall values from the YOLOX-tiny model are 0.980, 0.95, and 0.983, respectively. Meanwhile, the intermediate variable memory generated during the model calculation of YOLOX-tiny is only 259.62 MB, making it suitable for deployment in intelligent agricultural devices. However, although the YOLOX-tiny model is the best on the dataset in this paper, this is not true in general. The experimental results suggest that the method proposed in this paper can improve the model performance for the small target detection of sheltered weeds and dense weeds at the rice seedling stage in paddy fields. A weed target detection model suitable for embedded computing platforms is obtained by comparing different single-stage target detection models, thereby laying a foundation for the realization of unmanned targeted herbicide spraying performed by agricultural robots.

杂草是水稻生长的最大威胁之一，在水稻生长的早期阶段，作物的损失更大。传统的大面积喷洒不能有针对性地喷洒杂草，容易造成除草剂浪费和环境污染。为了实现从大面积喷洒向稻田精确喷洒的转变，需要快速有效地检测杂草的分布。受益于视觉技术和深度学习的快速发展，本研究应用了一种基于深度学习驱动的稻田杂草目标检测的计算机视觉方法。为了解决在稻田中识别水稻幼苗阶段小而密集的目标的需要，本研究提出了一种基于 YOLOX 的杂草目标检测方法，该方法由 CSPDarknet 骨干网络、特征金字塔网络（FPN）增强特征提取网络和 YOLO 头检测器组成。CSPDarknet 骨干网络提取维度为 80 像素 ⊆ 80 像素、40 像素 ⊆ 40 像素和 20 像素 ⊆ 20 像素的特征层。FPN 融合了这三个尺度的特征，YOLO 头实现了物体分类和预测框的回归。在不同模型的性能比较中，包括 YOLOv3、YOLOv4-tiny、YOLOv5-s、SSD 和 YOLOX 系列的几个模型，即 YOLOX-s、YOLOX-m、YOLOX-nano 和 YOLOX-tiny，结果表明 YOLOX-tiny 模型表现最好。YOLOX-tiny 模型的 mAP、F1 和召回值分别为 0.980、0.95 和 0.983，同时，YOLOX-tiny 模型在计算过程中产生的中间变量内存仅为 259.62MB，适合部署在智能农业设备中。然而，尽管 YOLOX-tiny 模型在本文的数据集中表现最好，但这并不普遍适用。实验结果表明，本文提出的方法可以提高模型对稻田中隐蔽杂草和密集杂草的小目标检测性能。通过比较不同的单阶段目标检测模型，获得了适合嵌入式计算平台的杂草目标检测模型，为农业机器人实现无人靶向施药喷洒奠定了基础。