Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device

In this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast. While many researchers have paid much attention to the flower classification in existing studie...

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Main Authors: Zhibin Cheng, Fuquan Zhang
Format: Article
Language:English
Published: Hindawi-Wiley 2020-01-01
Series:Wireless Communications and Mobile Computing
Online Access:http://dx.doi.org/10.1155/2020/8870649
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spelling doaj-97971c33793e4b9cb785ad464a4e26fa2020-11-25T03:42:49ZengHindawi-WileyWireless Communications and Mobile Computing1530-86691530-86772020-01-01202010.1155/2020/88706498870649Flower End-to-End Detection Based on YOLOv4 Using a Mobile DeviceZhibin Cheng0Fuquan Zhang1Educational Administration and Scientific Research, Fujian Polytechnic of Information Technology, Fuzhou, Fujian 350003, ChinaCollege of Computer and Control Engineering, Minjiang University, Fuzhou 350108, ChinaIn this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast. While many researchers have paid much attention to the flower classification in existing studies, the issue of flower detection has been largely overlooked. The problem we have outlined deals largely with the study of a new design and application of flower detection. Firstly, a new end-to-end flower detection anchor-based method is inserted into the architecture of the network to make it more precious and fast and the loss function and attention mechanism are introduced into our model to suppress unimportant features. Secondly, our flower detection algorithms can be integrated into the mobile device. It is revealed that our flower detection method is very considerable through a series of investigations carried out. The detection accuracy of our method is similar to that of the state-of-the-art, and the detection speed is faster at the same time. It makes a major contribution to flower detection in computer vision.http://dx.doi.org/10.1155/2020/8870649
collection DOAJ
language English
format Article
sources DOAJ
author Zhibin Cheng
Fuquan Zhang
spellingShingle Zhibin Cheng
Fuquan Zhang
Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device
Wireless Communications and Mobile Computing
author_facet Zhibin Cheng
Fuquan Zhang
author_sort Zhibin Cheng
title Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device
title_short Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device
title_full Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device
title_fullStr Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device
title_full_unstemmed Flower End-to-End Detection Based on YOLOv4 Using a Mobile Device
title_sort flower end-to-end detection based on yolov4 using a mobile device
publisher Hindawi-Wiley
series Wireless Communications and Mobile Computing
issn 1530-8669
1530-8677
publishDate 2020-01-01
description In this paper, a novel flower detection application anchor-based method is proposed, which is combined with an attention mechanism to detect the flowers in a smart garden in AIoT more accurately and fast. While many researchers have paid much attention to the flower classification in existing studies, the issue of flower detection has been largely overlooked. The problem we have outlined deals largely with the study of a new design and application of flower detection. Firstly, a new end-to-end flower detection anchor-based method is inserted into the architecture of the network to make it more precious and fast and the loss function and attention mechanism are introduced into our model to suppress unimportant features. Secondly, our flower detection algorithms can be integrated into the mobile device. It is revealed that our flower detection method is very considerable through a series of investigations carried out. The detection accuracy of our method is similar to that of the state-of-the-art, and the detection speed is faster at the same time. It makes a major contribution to flower detection in computer vision.
url http://dx.doi.org/10.1155/2020/8870649
work_keys_str_mv AT zhibincheng flowerendtoenddetectionbasedonyolov4usingamobiledevice
AT fuquanzhang flowerendtoenddetectionbasedonyolov4usingamobiledevice
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