基于改进YOLOv5的田间大豆花朵生长状态识别方法研究

Research on the identification method of soybean flower growth status in the field based on improved YOLOv5

为判断大豆开花时期落花情况,对田间大豆花朵在花蕾、半开、全开、凋落四类生长状态下进行精准检测。基于YOLOv5检测模型,对主干Bottleneck CSP结构进行修改,减少模块数量来保留更多浅层特征,增强特征表达能力,在骨干网络中引入CA注意机制,以获得位置信息,协助模型更加准确地识别,并修改锚箱尺寸提高小目标花蕾精准识别,在自建的田间大豆花朵不同生长状态数据集上进行改进YOLOv5算法对比试验。结果表明:大豆开花时期花朵不同生长状态识别模型准确率达到93.4%,召回率达到91.4%,对比原模型准确率、召回率分别提高0.8%和2.1%。

In order to judge the fall of soybean flowers during the flowering period,soybean flowers in the field were accurately detected under four growth states such as flower bud,half-opening,full-opening and withering.Based on the YOLOv5 detection model,the backbone Bottleneck CSP structure was modified,the number of modules was reduced to preserve more shallow features and enhance feature expression ability.CA attention mechanism was introduced into the backbone network to obtain location information and help the model identify more accurately.Moreover,the size of anchor box was modified to improve the accurate identification of small target bud,and the improved YOLOv5 algorithm was compared with the self-built data set of different growth states of soybean flowers in the field.The results showed that the accuracy rate of the model reached 93.4%and the recall rate reached 91.4%,which were increased by 0.8%and 2.1%respectively compared with the original model.