南京农业大学学报2024，Vol.47Issue(4)：772-781,10.DOI:10.7685/jnau.202312020
基于视觉自注意力模型的苗期玉米与杂草检测方法
Detection method of maize and weeds at seedling stage based on visual self-attention model
摘要
Abstract
[Objectives]Identifying crops and weeds are crucial aspects of advancing intelligent and automated weeding.This article aimed to improve the accuracy of crop and weed identification,to enhance the real-time performance of detection models,and to enhance robustness.[Methods]Focusing on maize crops and their corresponding weeds in the leaf age range of 3-8 leaves,this research endeavored to devise a detection method for maize seedlings and associated grasses.The seedling detection method leveraged an improved real-time end-to-end object detection with transformers(RT-DETR)for maize and weed detection in field conditions.The novel concept of replacing large-scale deep convolution with small-scale convolution equivalence within RT-DETR was introduced,reducing training complexity and inference time while maintaining detection accuracy.Furthermore,a self-attention mechanism with contextual information was integrated to enhance target attention and improve small target detection.Additionally,a combined image enhancement strategy was employed to enhance model accuracy and generalization.[Results]The improved model effectively distinguished weeds from crops in complex field scenarios,achieving an average detection accuracy of 90.11%.In the inference stage,each image took 33.67 ms for processing,with a model size of 44.86 MB.Compared with the mainstream target detection model,the improved model had higher overall accuracy and fast speed.[Conclusions]The proposed method had excellent overall detection performance for corn seedlings and associated weeds,which could improve the accuracy and efficiency of weed identification.关键词
玉米/杂草/检测/实时视觉自注意力模型/等效卷积/图像增强Key words
corn/weed/detection/end-to-end object detection with transformers/convolutional equivalence/image augmentation分类
农业科技引用本文复制引用
唐伯青,赵大勇,熊锋,李德强..基于视觉自注意力模型的苗期玉米与杂草检测方法[J].南京农业大学学报,2024,47(4):772-781,10.基金项目
中国科学院战略性先导专项(XDA28040400) （XDA28040400）
