摘要
为了解决目标检测区域中冗余信息过多导致无法准确检测大豆分枝的缺陷,同时快速获取大豆植株表型参数,该研究提出了一种基于Re-YOLOv5和检测区域搜索算法的大豆植株表型参数获取方法。Re-YOLOv5引入圆形平滑标签技术(Circular Smooth Label,CSL)实现旋转目标检测,解决了传统目标检测中检测区域冗余信息过多导致无法准确检测大豆分枝的缺陷,并加入协调注意力机制(Coordinate Attention,CA)获取目标位置信息以提升检测精度,此外,将原始骨干网络中的3×3卷积结构替换为RepVGG结构进一步增强模型的特征提取能力。基于Re-YOLOv5提出一种检测区域搜索算法(Detection Area Search,DAS),该算法将检测到的大豆分枝区域作为待搜索区域,通过该区域中的茎节点坐标信息判断各分枝的茎节点,然后将其进行顺序连接,重构大豆植株骨架,最终获取相关的表型参数。试验结果表明,Re-YOLOv5可以实现检测旋转目标的能力,而且在各项性能指标上都优于YOLOv5,其m AP提升了1.70个百分点,参数量下降0.17 M,针对茎节点的检测精确率提升了9.90个百分点,检测小目标的能力明显增强。检测区域搜索算法也能够准确地定位每个分枝上的茎节点从而重构大豆植株骨架,并得到比较准确的大豆植株表型参数,其中,株高、茎节点数、大豆分枝数的平均绝对误差分别为2.06 cm、1.37个和0.03个,在能够满足实际采集的精度要求的同时,也为获取大豆植株表型信息提供参考。
The phenotypic information of soybean has been one of the most important indicators for the variety selection of soybean.Most research has been focused on soybean pods for phenotypic traits at present.However,the phenotypic information of soybean plants can also be a very important indicator for soybean seed breeding.Furthermore,the current manual measurement cannot fully meet the large-scale production in recent years,due to the time and labor-consuming.More recently,computer technologies have been started to automatically acquire the soybean phenotypic parameters.For instance,some shallow machine learning and image description were used for the image feature extraction and target detection,such as the Scale-Invariant Feature Transform(SIFT),Histogram of Oriented Gradient(HOG),and Support Vector Machine(SVM).Although the automatic acquisition of phenotypic data was realized to a certain extent,the high generalization ability can be a high demand suitable for practical application scenarios.In this study,an improved model was proposed to rapidly and accurately acquire the soybean plant phenotype using Re-YOLOv5 and area search algorithm.The circular smooth labels were also introduced in the Re-YOLOv5 to process the angle information for the detection of the rotating objects.The angle regression was then converted into a simple classification.As such,the rotating objects were accurately detected to reduce the redundant information in the detection area during object detection.In addition,the Coordinate attention mechanism was added to the Neck part of YOLOv5(CSL).The cross-channel information was then obtained to capture the position and orientation information.As such,the improved network was used to more accurately locate and recognize the soybean branching than before.The weights greatly contributed to the YOLOv5 on the processing of feature-related parts.More importantly,the original 3×3 convolution kernels were placed in the backbone with a RepVGG block structure.The feature information was then fused to extract using different convolution modules.The information extraction of the overall structure was enhanced for the parallel fusion of the multiple convolution layers while reducing the number of model parameters.Taking the detected branch as the search area,an area search algorithm was also proposed to input into the algorithm,in order to extract the relevant information of the stem nodes in the area,and then connect the nodes in sequence.Thus,the soybean skeleton was reconstructed to obtain phenotypic information about soybean.The experimental results showed that the improved Re-YOLOv5 performed better to detect the rotating objects in various phenotypic indicators,compared with the YOLOv5.Specifically,the mAP of the improved Re-YOLOv5 increased by 1.70 percentage points,the number of parameters decreased by 0.17 M,and the detection accuracy of stem nodes was improved by 9.90 percentage points.An excellent ability was also achieved to detect the small targets suitable for the acquisition of the soybean plant phenotype information.Among them,the average absolute errors of plant height,and the number of stem nodes and branches were 2.06 cm,1.37,and 0.03,respectively,fully meeting the accuracy requirements of actual collection.At the same time,the detection area search algorithm can also be expected to accurately locate the stem nodes on each branch for the single-,double-,and complex-branched soybean plants,and then reconstruct an accurate soybean skeleton.Anyway,the improved model can also be used to accurately and efficiently acquire the phenotypic information of soybean branch angle,and soybean plant type.The finding can provide a strong reference for the subsequent acquisition of phenotypic information during soybean production.
作者
郭希岳
李劲松
郑立华
张漫
王敏娟
Guo Xiyue;Li Jinsong;Zheng Lihua;Zhang Man;Wang Minjuan(Key Lab of Smart Agriculture Systems,Ministry of Education,China Agricultural University,Beijing 100083,China;Yantai Institute of China Agricultural University,Yantai 264670,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第15期186-194,共9页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(31971786)
山东省自然科学基金面上项目(ZR2021MC021)。
