| 摘要: |
| 【目的】 为实现果园自然场景下智能农业机器人对桃花的准确、快速、有效检测。【方法】 文章采用相机获取桃花图片数据，通过LabelImg软件进行人工标记建立桃花目标识别的检测样本数据集，训练Darknet深度学习框架下的YOLO v4模型对桃花进行识别。【结果】 模型精度评估表明，YOLO v4模型的平均准确率MAP值（86%）比Faster R-CNN的MAP值（51%）高出35%。【结论】 YOLO v4与经典的算法相比，对各种自然环境下的桃花检测具有较好的实时性和鲁棒性，可为精准识别桃花提供重要参考价值，桃花精准识别为疏花疏果作业奠定了基础。 |
| 关键词: Darknet Faster R-CNN 桃花识别 目标检测 自然场景 |
| DOI：10.12105/j.issn.1672-0423.20210603 |
| 分类号: |
| 基金项目:四川省科技计划项目“农业大数据资产管理及智能分析应用系统”(2021YFG0028) |
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| The peach blossom detection method based on darknet deep learning framework |
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Guo Tao1, Guo Jia2, Li Zongnan1, Qiu Xia1, Wang Si1
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1.Institute of Remote Sensing and Digital Agriculture，Sichuan Academy of Agricultural Sciences，Chengdu 610066，China;2.The Academy of Digital China（Fujian），Fu Jian Fuzhou 350116，China
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| Abstract: |
| [Purpose] The purpose is realize the accurate，rapid and effective detection of peach blossom by intelligent agricultural robot in natural orchard scene.[Method] the camera is used to get peach blossom’s image data，and LabelImg software is used for manual marking to establish a detection sample dataset for peach blossom target identification，and YOLO v4 model under Darknet deep learning framework is trained to identify peach blossom.[Result] The accuracy evaluation of the model showed that the MAP accuracy of YOLO v4 model is 86%，35% higher than that of Faster R-CNN，which was 51%.[Conclusion] Compared with the traditional algorithm，the YOLO v4 algorithm has better real-time performance and robustness for peach blossom detection in various natural environments，which has important reference value for accurate peach blossom recognition identification which further lays a foundation for accurate peach blossom and fruit thinning. |
| Key words: Darknet Faster R-CNN peach blossom identification target detection natural scene |
