| 张合涛，赵春江，王传宇,郭新宇，李大壮,苟文博.基于卷积神经网络的田间麦穗检测方法研究[J].麦类作物学报,2023,(6):798 |
| 基于卷积神经网络的田间麦穗检测方法研究 |
| Study on Wheat Ear Detection Method Based on Convolution Neural Network |
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| DOI： |
| 中文关键词: 小麦麦穗 卷积神经网络 特征提取 特征融合 损失函数 麦穗识别检测模型 |
| 英文关键词:Wheat ears Convolutional neural network Feature extraction Feature fusion Loss function Wheat ears recognition and detection model |
| 基金项目:北京市农林科学院作物表型组学协同创新中心资助项目 |
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| 摘要点击次数: 244 |
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| 中文摘要: |
| 为提高卷积神经网络对麦穗的识别检测精度，在YOLOv5检测模型基础上提出改进的识别检测模型YOLOv5-αTB，在特征提取网络末端部分加入Transformer模块，强化特征提取网络对小麦麦穗图像的颜色、纹理、几何等特征的提取，在特征融合部分将路径聚合网络（path aggregation network，PANet）替换成加权双向特征金字塔（bidirectional feature pyramid network，BiFPN），进一步优化多尺度特征的融合。针对边界框回归损失函数的计算方式IoU的局限性，引入了α-CIoU加强了边界框回归的效果。利用YOLOv5-αTB检测模型在测试集上得到的精确度（precision）、召回率（recall）和平均精度(average precision，AP)分别是99.95%、81.86%和88.64%，在平均精度上相比于传统的YOLOv5模型提升2.92个百分点。该模型检测统计麦穗数量对比人工计数结果，识别检测精度约为97.00%。 |
| 英文摘要: |
| In order to improve the recognition and detection accuracy of convolutional neural network for wheat ears, an improved recognition and detection model YOLOv5-αTB was proposed based on the YOLOv5 detection model. Transformer module was added to the end part of feature extraction network to enhance the extraction of color, texture and feature of wheat ear image. In the feature fusion part, the Path Aggregation Network (PANet) was replaced by the weighted Bidirectional Feature Pyramid Network (BiFPN) to further optimize the multi-scale feature fusion. Aiming at the limitation of IoU, α-CIOU was introduced to enhance the effect of boundary box regression. The precision, recall, and average precision (AP) obtained by YOLOv5-αTB detection model on the test set are 99.95%, 81.86%, and 88.64%, respectively. Compared with the traditional YOLOv5 model, the average precision of the model was improved by 2.92 percentage points. Compared with the results of manual counting, the detection precision of the model was about 97.00%. |
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