准确、实时的田间小麦穗数对小麦产量预测具有重要意义,遗传育种和优化种植管理。为了实现大分辨率无人机视频下的麦穗检测和计数,深度学习模型YOLOv7x中增加了空间到深度(SPD)模块。归一化高斯Wasserstein距离(NWD)损失函数旨在创建新的检测模型YOLOv7xSPD。精度,召回,模型在测试集上的F1得分和AP为95.85%,94.71%,95.28%,94.99%,分别。AP值比YOLOv7x高1.67%,和10.41%,39.32%,2.96%,比更快的RCNN高出0.22%,SSD,YOLOv5s,YOLOV7YOLOv7xSPD结合卡尔曼滤波跟踪和匈牙利匹配算法,建立了具有视频流,称为YOLOv7xSPD计数器,可以实现田间麦穗的实时计数。在分辨率为3840×2160的视频中,YOLOv7xSPD计数器的检测帧速率约为5.5FPS。计数结果与地面实况数高度相关(R2=0.99),为小麦产量预测提供模型依据,遗传育种和优化种植管理。
Accurate and real-time field wheat ear counting is of great significance for wheat yield prediction, genetic breeding and optimized planting management. In order to realize wheat ear detection and counting under the large-resolution Unmanned Aerial Vehicle (UAV) video, Space to depth (SPD) module was added to the deep learning model YOLOv7x. The Normalized Gaussian Wasserstein Distance (NWD) Loss function is designed to create a new detection model YOLOv7xSPD. The precision, recall, F1 score and AP of the model on the test set are 95.85%, 94.71%, 95.28%, and 94.99%, respectively. The AP value is 1.67% higher than that of YOLOv7x, and 10.41%, 39.32%, 2.96%, and 0.22% higher than that of Faster RCNN, SSD, YOLOv5s, and YOLOv7. YOLOv7xSPD is combined with the Kalman filter tracking and the Hungarian matching algorithm to establish a wheat ear counting model with the video flow, called YOLOv7xSPD Counter, which can realize real-time counting of wheat ears in the field. In the video with a resolution of 3840×2160, the detection frame rate of YOLOv7xSPD Counter is about 5.5FPS. The counting results are highly correlated with the ground truth number (R2 = 0.99), and can provide model basis for wheat yield prediction, genetic breeding and optimized planting management.