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Open AccessArticle

Real-Time Detection for Wheat Head Applying Deep Neural Network

Key Laboratory of Electronic and Information Engineering (Southwest Minzu University), State Ethnic Affairs Commission, Chengdu 610041, China
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Sensors 2021, 21(1), 191; https://doi.org/10.3390/s21010191
Received: 5 December 2020 / Revised: 25 December 2020 / Accepted: 28 December 2020 / Published: 30 December 2020
(This article belongs to the Section Sensing and Imaging)
Wheat head detection can estimate various wheat traits, such as density, health, and the presence of wheat head. However, traditional detection methods have a huge array of problems, including low efficiency, strong subjectivity, and poor accuracy. In this paper, a method of wheat-head detection based on a deep neural network is proposed to enhance the speed and accuracy of detection. The YOLOv4 is taken as the basic network. The backbone part in the basic network is enhanced by adding dual spatial pyramid pooling (SPP) networks to improve the ability of feature learning and increase the receptive field of the convolutional network. Multilevel features are obtained by a multipath neck part using a top-down to bottom-up strategy. Finally, YOLOv3′s head structures are used to predict the boxes of wheat heads. For training images, some data augmentation technologies are used. The experimental results demonstrate that the proposed method has a significant advantage in accuracy and speed. The mean average precision of our method is 94.5%, and the detection speed is 71 FPS that can achieve the effect of real-time detection. View Full-Text
Keywords: deep learning; wheat head; real-time object detection; SPP deep learning; wheat head; real-time object detection; SPP
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MDPI and ACS Style

Gong, B.; Ergu, D.; Cai, Y.; Ma, B. Real-Time Detection for Wheat Head Applying Deep Neural Network. Sensors 2021, 21, 191. https://doi.org/10.3390/s21010191

AMA Style

Gong B, Ergu D, Cai Y, Ma B. Real-Time Detection for Wheat Head Applying Deep Neural Network. Sensors. 2021; 21(1):191. https://doi.org/10.3390/s21010191

Chicago/Turabian Style

Gong, Bo; Ergu, Daji; Cai, Ying; Ma, Bo. 2021. "Real-Time Detection for Wheat Head Applying Deep Neural Network" Sensors 21, no. 1: 191. https://doi.org/10.3390/s21010191

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