基于纹理感知模块改进的杂交水稻制繁种杂株检测方法  

Hybrid Rice Breeding Abnormal Plant Detection Method Improved on Texture Cognition Module

作　　者：杨惇泓 王永维[1] 王俊[1] YANG Dunhong;WANG Yongwei;WANG Jun(Department of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou 310058,China)

机构地区：[1]浙江大学生物系统工程与食品科学学院,杭州310058

出　　处：《农业机械学报》2024年第S2期286-293,共8页Transactions of the Chinese Society for Agricultural Machinery

基　　金：国家重点研发计划项目(2023YFD2000402);国家现代农业产业技术体系项目(CARS-01);浙江省“三农九方”农业科技协作计划项目(2023SNJF048);浙江大学科研项目(XY2023042)

摘　　要：除杂是杂交水稻制繁种过程中保证种子纯度的关键步骤。为了防止杂株产生异常花粉影响杂交优势,除杂作业需要反复人工操作,耗费大量的人工和时间。田间杂株的自动化地识别是实现机械化和自动化除杂的基础。为了实现杂交水稻制繁种杂株的自动化精确检测,首先使用无人机航拍采集含有杂株的杂交水稻制繁种田俯拍图像,通过中心裁剪获得无畸变的高质量图像,标注出图像中的杂株目标后经过几何变化和颜色变化进行数据增强,获得杂交水稻制繁种田间杂株数据集。针对图像数据集中杂株和正常植株之间的高相似度,提出了一种杂株目标检测网络模型T-CenterNet2,在CenterNet2网络的特征金字塔网络中添加纹理感知模块,这一模块通过重组通道信息获得特征图中的纹理特征,进而增强杂株目标和背景的特征差异;并重新设计了损失函数,添加测量纹理特征和标签真值之间差异的纹理损失,用于控制纹理感知模块;针对除杂的实际作业情况引入DIoU作为边界框损失,通过增加预测框和标签中心点的距离惩罚项以提高网络预测的目标中心点准确度。为了验证各项改进对模型的性能提升,首先使用mAP和召回率作为评价指标描述模型对杂株目标的检测效果,将改进后模型与原始模型CenterNet2以及4种典型模型(Faster R-CNN、FCOS、YOLOX、DeTR)进行对比,实验结果表明改进后T-CenterNet2模型mAP达到86.4%,较原始模型提高11.0个百分点,召回率达到82.5%,较原始模型提高11.6个百分点,而典型模型最高mAP和召回率仅为73.1%和66.2%,T-CenterNet2模型取得明显的优势。其次对比了不同损失函数组合对模型收敛速度和检测精度的影响,其中具有权重的纹理损失和DIoU组取得最佳结果,证明重新设计的损失函数有效适用于杂株检测任务。改进后模型具有较高的检测精度和鲁棒性,能够实现良好的杂株检测Abnormal plant removal is a critical step in ensuring seed purity during hybrid rice seed production.To prevent abnormal plants from producing abnormal pollen that could compromise hybrid vigor,current abnormal plant removal operations require repeated manual efforts,consuming significant time and labor.The automation of abnormal plant identification in the field is fundamental to achieve mechanized and automated removal.Aiming to achieve automated and precise detection of abnormal plant in hybrid rice seed production,UAV aerial images of hybrid rice seed production fields containing abnormal plants were collected,and high-quality and distortion-free images were obtained through center cropping.The abnormal plants in the images were annotated,and data augmentation was performed through geometric and color transformations to create a dataset of abnormal plants in hybrid rice seed production fields.To address the high similarity between abnormal and normal plants in the image dataset,a novel abnormal plant detection network model,T-CenterNet2,was proposed.This model enhanced the CenterNet2 network by incorporating a texture-aware module within the feature pyramid network,which reorganized channel information to extract texture features from the feature maps,thereby increasing the feature distinction between abnormal plants and the background.Additionally,a combination of loss functions was designed,including a texture loss that measured the difference between texture features and label ground truth to control the texture-aware module.DIoU was introduced as the bounding box loss to improve the accuracy of target center point predictions,in line with the practical requirements of abnormal plant removal operations.The effects of different loss function combinations on model convergence speed and detection accuracy were compared,with the combination of weighted texture loss and DIoU yielding the best results,demonstrating the effectiveness of the redesigned loss function for abnormal plant detection tasks.Using mAP and

关 键 词：杂交水稻 异常检测 目标检测 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]