Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (6): 271-277.DOI: 10.3778/j.issn.1002-8331.2108-0077

• Engineering and Applications • Previous Articles     Next Articles

Field Weed Identification Method Based on Deep Connection Attention Mechanism

SHU Yali, ZHANG Guowei, WANG Bo, XU Xiaokang   

  1. School of Automation Engineering, Shanghai University of Electric Power, Shanghai 200082, China
  • Online:2022-03-15 Published:2022-03-15



  1. 上海电力大学 自动化工程学院,上海 200082

Abstract: In order to achieve fast and accurate recognition of field weed images, a field weed recognition model based on deep connected attention mechanism residual network(DCECA-Resnet50-a) is proposed. Using the residual network as a benchmark, this paper improves the position of residual block downsampling, introduces the attention mechanism and connected attention mechanism modules to better extract the feature information in the images, combines the migration learning strategy to alleviate the overfitting phenomenon caused by small sample data sets, improves the generalization of the model and greatly reduces the training time of the model. The experimental results show that the improved model has the best overall performance and high recognition accuracy, with 96.31% accuracy for weeds and fewer model parameters, and achieves the accurate differentiation of four types of common weeds in pea fields, namely, silverleaf daisy, chaparral, matang and pigweed, which provides a corresponding reference for small sample data in the field of agricultural recognition.

Key words: image recognition, residual networks, attention mechanism, migration learning

摘要: 为了实现田间杂草图像快速、准确识别,提出了一种基于深层连接注意力机制残差网络(DCECA-Resnet50-a)的田间杂草识别模型。以残差网络为基准,改进残差块下采样的位置,同时引入注意力机制和连接注意力机制模块以更好地提取图片中的特征信息,结合迁移学习的策略缓解小样本数据集造成的过拟合现象,提高模型的泛化性并大大减少模型的训练时长。实验结果表明,改进后的模型综合性能最好,有较高的识别准确率,对杂草的识别准确率达到了96.31%且模型参数较少,实现了对银叶菊、小蓬草、马唐和猪殃殃四类豌豆田间常见杂草的准确区分,为农业的小样本数据在识别领域中提供了相应的参考作用。

关键词: 图像识别, 残差网络, 注意力机制, 迁移学习