Li Wenwen, Zhang Yun
School of Mechanical and Control Engineering, BaiCheng Normal University, BaiCheng, 137000, China.
School of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132022, China.
Sci Rep. 2024 Nov 2;14(1):26430. doi: 10.1038/s41598-024-77865-x.
Weeding is an important part of agricultural production. With the development of science and technology, automated weeding is regarded as the future development direction, and how to accurately and efficiently detect plants in the field is one of the key points. Corn seedlings and weeds are similar in color, shape and other characteristics, which brings serious challenges to plant detection. In this paper, we propose an improved model based on YOLOv7-tiny, called DC-YOLO. To improve the extraction of key features in the model, we propose Dual Coordinate Attention model (DCA). In addition, we introduce the Content-Aware ReAssembly of FEatures (CARAFE) operator to represent the up-sampling process as a learnable feature reorganization, which enriches the feature information of the sampled images. Finally, we decoupled the detection head to minimize conflicts between features from different tasks. The results show that applying the proposed method to corn and weed datasets, the detection accuracy of the model reaches 95.7% mean Average Precision (mAP@0.5), the computational effort of the model is 13.083 Giga Floating-point Operations (GFLOPs), and the parameter size is 5.223 Millon (M), which is better than the rest of the mainstream light-weight target detection model.
除草是农业生产的重要组成部分。随着科技的发展，自动除草被视为未来的发展方向，而如何在田间准确高效地检测植物是关键要点之一。玉米幼苗和杂草在颜色、形状等特征上相似，这给植物检测带来了严峻挑战。在本文中，我们提出了一种基于YOLOv7-tiny的改进模型，称为DC-YOLO。为了提高模型中关键特征的提取能力，我们提出了双坐标注意力模型（DCA）。此外，我们引入了特征内容感知重组（CARAFE）算子，将上采样过程表示为可学习的特征重组，丰富了采样图像的特征信息。最后，我们对检测头进行解耦，以最小化不同任务特征之间的冲突。结果表明，将所提出的方法应用于玉米和杂草数据集时，模型的检测准确率达到95.7%的平均精度均值（mAP@0.5），模型的计算量为13.083吉咖浮点运算（GFLOPs），参数大小为522.3万（M），优于其他主流轻量级目标检测模型。
