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A Self-Attention Feature Fusion Model for Rice Pest Detection

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成果类型:
期刊论文
作者:
Li, Shuaifeng;Wang, Heng;Zhang, Cong;Liu, Jie
通讯作者:
Wang, H.
作者机构:
[Liu, Jie; Wang, Heng; Li, Shuaifeng] Wuhan Polytech Univ, Sch Math & Comp, Wuhan 430048, Peoples R China.
[Liu, Jie; Wang, Heng; Li, Shuaifeng] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430073, Peoples R China.
[Zhang, Cong] Wuhan Polytech Univ, Sch Elect & Elect Engn, Wuhan 430048, Peoples R China.
通讯机构:
[Wang, H.] W
Wuhan Polytechnic University, School of Mathematics and Computer, Wuhan, China
语种:
英文
关键词:
Feature extraction;Convolution;Shape;Standards;Biological system modeling;Convolutional neural networks;Semantics;Pest detection;object detection;deep learning;computer vision;SAFFPest model
期刊:
IEEE ACCESS
ISSN:
2169-3536
年:
2022
卷:
10
页码:
84063-84077
基金类别:
10.13039/501100001809-National Natural Science Foundation (Grant Number: 61401319 and 61272278) 10.13039/501100003819-Hubei Province Natural Science Foundation (Grant Number: 2014CFB270 and 2015CFA061) 10.13039/100012554-Hubei Provincial Department of Education Research Foundation (Grant Number: D20201601) Hubei Provincial Major Science and Technology Special Projects (Grant Number: 2018ABA099) 10.13039/501100008869-Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology (Grant Number: HBIR202101)
机构署名:
本校为第一机构
院系归属:
电气与电子工程学院
数学与计算机学院
摘要:
To address the problem that existing deep learning methods are not sufficiently accurate to detect rice pests with changeable shapes or similar appearances, a self-attention feature fusion model for rice pest detection (SAFFPest) was proposed. The model was based on VarifocalNet. First, a deformable convolution module was added to the feature extraction network, to improve the feature extraction ability of pests with changeable shapes. Second, by obtaining the balance features of multiple feature maps, the self-attention mechanism was introduce...

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