关键词: 农田杂草检测, 深度可分离卷积, 多尺度注意力, 形态图像特征

Abstract: Field weed eradication is the premise of promoting stable agricultural production. The diversity of weed species and the diversity of size, color, and location of the same species result in the unsatisfactory performance of traditional weed detection algorithms. A field weed detection algorithm based on multiscale attention and depth separable convolution is proposed in this paper. Firstly, the backbone network VGG-16 is improved by deep separable convolution to reduce the number of model parameters and speed up the model training. Then, the multiscale attention module is used to extract the multiscale features of weeds to enhance the ability of the proposed model to capture morphological image features. The results show that the precision of the proposed algorithm is 9469%, the recall is 9488%, and the F1 is 9382%. Compared with the current mainstream weed detection models, the proposed algorithm has a lower time overhead while maintaining higher detection performance. It can be used for the automatic detection of weeds in farmland.

Key words: field weed recognition, depth separable convolution, multiscale attention, morphological image features