Winter wheat ear counting based on improved YOLOv7x and Kalman filter tracking algorithm with video streaming

文献类型： 外文期刊

第一作者： Xu, Xingmei

作者： Xu, Xingmei;Zhou, Lei;Yu, Helong;Sun, Guangyao;Fei, Shuaipeng;Zhu, Jinyu;Sun, Guangyao;Fei, Shuaipeng;Ma, Yuntao

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关键词： wheat ear counting; real-time detection; YOLOv7x; Kalman filter; UAV

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.1； 五年影响因子：5.3 ）

ISSN： 1664-462X

年卷期： 2024 年 15 卷

页码：

收录情况： SCI

摘要： 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 3840x2160, 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.

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