摘要
【目的】提高杂交稻种子活力分级检测精度和速度。【方法】提出了一种基于YOLOv5改进模型(YOLOv5-I)的杂交稻芽种快速分级检测方法,该方法引入SE(Squeeze-and-excitation)注意力机制模块以提高目标通道的特征提取能力,并采用CIoU损失函数策略以提高模型的收敛速度。【结果】YOLOv5-I算法能有效实现杂交稻芽种快速分级检测,检测精度和准确率高,检测速度快。在测试集上,YOLOv5-I算法目标检测的平均精度为97.52%,平均检测时间为3.745 ms,模型占用内存空间小,仅为13.7 MB;YOLOv5-I算法的检测精度和速度均优于YOLOv5s、Faster-RCNN、YOLOv4和SSD模型。【结论】YOLOv5-I算法优于现有的算法,提升了检测精度和速度,能够满足杂交稻芽种分级检测的实用要求。
【Objective】In order to improve the grading detection accuracy and speed of hybrid rice seed vigor.【Method】A rapid grading detection method for hybrid rice bud seeds named YOLOv5-I model,which was an improved model based on YOLOv5,was proposed.The feature extraction ability of the target channel of YOLOv5-I model was improved by introducing the SE(Squeeze-and-excitation)attention mechanism module,and a CIoU loss function strategy was adopted to improve the convergence speed of this model.【Result】The YOLOv5-I algorithm effectively achieved the rapid grading detection of hybrid rice bud seeds,with high detection accuracy and speed.In the test set,the average accuracy of the YOLOv5-I model was 97.52%,the average detection time of each image was 3.745 ms,and the memory space occupied by the YOLOv5-I model was small with 13.7 MB.The detection accuracy and speed of YOLOv5-I model was better than those of YOLOv5s,Faster-RCNN,YOLOv4 and SSD models.【Conclusion】The YOLOv5-I algorithm is better than existing algorithms,improves detection accuracy and speed,and can meet the practical requirement for grading detection of hybrid rice bud seeds.
作者
钟海敏
马旭
李泽华
王曦成
刘赛赛
刘伟文
王承恩
林泳达
ZHONG Haimin;MA Xu;LI Zehua;WANG Xicheng;LIU Saisai;LIU Weiwen;WANG Cheng’en;LIN Yongda(College of Mathematics and Informatics,South China Agricultural University,Guangzhou 510642,China;College of Engineering,South China Agricultural University,Guangzhou 510642,China;Key Laboratory of Smart Agricultural Technology in Tropical South China,Ministry of Agriculture and Rural Affairs,P.R.China,Guangzhou 510642,China;College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China)
出处
《华南农业大学学报》
CAS
CSCD
北大核心
2023年第6期960-967,共8页
Journal of South China Agricultural University
基金
国家自然科学基金(52175226)
岭南现代农业实验室科研项目(N T 2021009)
广东省科技厅项目(KTP20210196)
现代农业产业技术体系建设专项(CARS-01-47)。
