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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Series: | Wireless Communications and Mobile Computing |
Online Access: | http://dx.doi.org/10.1155/2020/8870649 |
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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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1715137682424725504 |