| 基于YOLOv5改进模型的杂交稻芽种快速分级检测 |
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| 引用本文： | 钟海敏,马旭,李泽华,王曦成,刘赛赛,刘伟文,王承恩,林泳达.基于YOLOv5改进模型的杂交稻芽种快速分级检测[J].华南农业大学学报,2023,44(6):960-967. |
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| 作者姓名： | 钟海敏 马旭 李泽华 王曦成 刘赛赛 刘伟文 王承恩 林泳达 |
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| 作者单位： | 华南农业大学 数学与信息学院, 广东 广州 510642;华南农业大学 工程学院, 广东 广州 510642;华南农业大学 数学与信息学院, 广东 广州 510642;农业农村部华南热带智慧农业技术重点实验室, 广东 广州 510642;华南农业大学 电子工程学院, 广东 广州 510642 |
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| 基金项目： | 国家自然科学基金(52175226)；岭南现代农业实验室科研项目(NT2021009)；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) |
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| 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。
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| 关 键 词： | 深度学习 YOLOv5s 分级检测 注意力机制 杂交稻 种子活力 |
| 收稿时间： | 2022/9/12 0:00:00 |
Rapid grading detection on hybrid rice bud seeds based on improved YOLOv5 model |
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| Institution: | College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China;College of Engineering, South China Agricultural University, Guangzhou 510642, China;College of Mathematics and Informatics, 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 |
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| Abstract: | 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. |
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| Keywords: | Deep learning YOLOv5s Grading detection Attention mechanism Hybrid rice Seed vigor |
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