改进YOLOv3算法检测三七叶片病害

TP391.4; 为了解决三七叶片密集病害和小区域病害检测不准确的问题,该研究提出了一种改进的YOLOv3(You Only Look Once v3)目标检测算法(AD-YOLOv3)对三七叶片各种病害进行检测.AD-YOLOv3使用注意力特征金字塔(Attention Feature Pyramid,AFP)替代YOLOv3中的原始特征金字塔,解决了特征融合过程中的干扰问题,提升了病害检测精度.使用双瓶颈层(Dual Bottleneck,DB)筛选注意力特征金字塔提取到的特征,增强特征的特异性,提升了算法的鲁棒性.AD-YOLOv3与YOLOv3相比在各项性能指标上均有提升,精确率提升2...

Full description

Saved in:
Bibliographic Details
Published in农业工程学报 Vol. 38; no. 3; pp. 164 - 172
Main Authors 文斌, 曹仁轩, 杨启良, 张健, 朱晗, 李知聪
Format Journal Article
LanguageChinese
Published 三峡大学电气与新能源学院,宜昌 443000%昆明理工大学现代农业工程学院,昆明 650500 01.02.2022
Subjects
算法:病害检测;YOLOv3;特征金字塔;双瓶颈层;注意力机制;三七
Online AccessGet full text

Cover

Abstract TP391.4; 为了解决三七叶片密集病害和小区域病害检测不准确的问题,该研究提出了一种改进的YOLOv3(You Only Look Once v3)目标检测算法(AD-YOLOv3)对三七叶片各种病害进行检测.AD-YOLOv3使用注意力特征金字塔(Attention Feature Pyramid,AFP)替代YOLOv3中的原始特征金字塔,解决了特征融合过程中的干扰问题,提升了病害检测精度.使用双瓶颈层(Dual Bottleneck,DB)筛选注意力特征金字塔提取到的特征,增强特征的特异性,提升了算法的鲁棒性.AD-YOLOv3与YOLOv3相比在各项性能指标上均有提升,精确率提升2.83个百分点,F1精度提升1.68个百分点,平均精度均值(Mean Average Precision,mAP)提升1.47个百分点,针对小区域病害和密集病害的检测能力明显增强.此外,AD-YOLOv3在雾,雨,暗光等复杂环境下的抗干扰能力明显提升,该研究为三七叶片的病害检测提供了一种更优的智能检测方法.
AbstractList TP391.4; 为了解决三七叶片密集病害和小区域病害检测不准确的问题,该研究提出了一种改进的YOLOv3(You Only Look Once v3)目标检测算法(AD-YOLOv3)对三七叶片各种病害进行检测.AD-YOLOv3使用注意力特征金字塔(Attention Feature Pyramid,AFP)替代YOLOv3中的原始特征金字塔,解决了特征融合过程中的干扰问题,提升了病害检测精度.使用双瓶颈层(Dual Bottleneck,DB)筛选注意力特征金字塔提取到的特征,增强特征的特异性,提升了算法的鲁棒性.AD-YOLOv3与YOLOv3相比在各项性能指标上均有提升,精确率提升2.83个百分点,F1精度提升1.68个百分点,平均精度均值(Mean Average Precision,mAP)提升1.47个百分点,针对小区域病害和密集病害的检测能力明显增强.此外,AD-YOLOv3在雾,雨,暗光等复杂环境下的抗干扰能力明显提升,该研究为三七叶片的病害检测提供了一种更优的智能检测方法.
Author 李知聪
文斌
朱晗
曹仁轩
杨启良
张健
AuthorAffiliation 三峡大学电气与新能源学院,宜昌 443000%昆明理工大学现代农业工程学院,昆明 650500
AuthorAffiliation_xml – name: 三峡大学电气与新能源学院,宜昌 443000%昆明理工大学现代农业工程学院,昆明 650500
Author_FL Cao Renxuan
Yang Qiliang
Wen Bin
Zhu Han
Li Zhicong
Zhang Jian
Author_FL_xml – sequence: 1
  fullname: Wen Bin
– sequence: 2
  fullname: Cao Renxuan
– sequence: 3
  fullname: Yang Qiliang
– sequence: 4
  fullname: Zhang Jian
– sequence: 5
  fullname: Zhu Han
– sequence: 6
  fullname: Li Zhicong
Author_xml – sequence: 1
  fullname: 文斌
– sequence: 2
  fullname: 曹仁轩
– sequence: 3
  fullname: 杨启良
– sequence: 4
  fullname: 张健
– sequence: 5
  fullname: 朱晗
– sequence: 6
  fullname: 李知聪
BookMark eNo9jztLA0EUhaeIYIz5GSIIO957Z_YxpQRfsLCNFlZhJrsTEmQCDr46QUSChY2SwtI-TVBi5Z9Jdv0ZrihWh3OK8_GtsYYbuYKxDQSOqOJwe8gH3juOABRECSpOQMRBcEDVYM3_fZW1vR8YCFHEABKbbKt8-vj6fDnJ0uxCVNNJOXsuX2_Kt4fFfLyY3y4f36vxfTW5W05n62zF6lNftP-yxY73do86B0Ga7R92dtLAI5AKYpUTWEk614KSODZUQI3GJEysAUIZAUprUIIulAp7KooKMCCFpbpCLlps8_f3UjurXb87HJ2fuZrYddf93pX5cQNRm4lv-QJTHA
ClassificationCodes TP391.4
ContentType Journal Article
Copyright Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
Copyright_xml – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
DBID 2B.
4A8
92I
93N
PSX
TCJ
DOI 10.11975/j.issn.1002-6819.2022.03.019
DatabaseName Wanfang Data Journals - Hong Kong
WANFANG Data Centre
Wanfang Data Journals
万方数据期刊 - 香港版
China Online Journals (COJ)
China Online Journals (COJ)
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Agriculture
DocumentTitle_FL Detecting leaf disease for Panax notoginseng using an improved YOLOv3 algorithm
EndPage 172
ExternalDocumentID nygcxb202203019
GrantInformation_xml – fundername: 国家自然科学基金
  funderid: (61876097)
GroupedDBID -04
2B.
4A8
5XA
5XE
92G
92I
93N
ABDBF
ABJNI
ACGFO
ACGFS
ACUHS
AEGXH
AIAGR
ALMA_UNASSIGNED_HOLDINGS
CCEZO
CHDYS
CW9
EOJEC
FIJ
IPNFZ
OBODZ
PSX
RIG
TCJ
TGD
TUS
U1G
U5N
ID FETCH-LOGICAL-s1029-79d20f42ada32877b2e08191858fb02146014fb140ae995c966e0b043f29950d3
ISSN 1002-6819
IngestDate Thu May 29 04:08:36 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 3
Keywords 算法:病害检测;YOLOv3;特征金字塔;双瓶颈层;注意力机制;三七
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-s1029-79d20f42ada32877b2e08191858fb02146014fb140ae995c966e0b043f29950d3
PageCount 9
ParticipantIDs wanfang_journals_nygcxb202203019
PublicationCentury 2000
PublicationDate 2022-02-01
PublicationDateYYYYMMDD 2022-02-01
PublicationDate_xml – month: 02
  year: 2022
  text: 2022-02-01
  day: 01
PublicationDecade 2020
PublicationTitle 农业工程学报
PublicationTitle_FL Transactions of the Chinese Society of Agricultural Engineering
PublicationYear 2022
Publisher 三峡大学电气与新能源学院,宜昌 443000%昆明理工大学现代农业工程学院,昆明 650500
Publisher_xml – name: 三峡大学电气与新能源学院,宜昌 443000%昆明理工大学现代农业工程学院,昆明 650500
SSID ssib051370041
ssj0041925
ssib001101065
ssib023167668
Score 2.3933136
Snippet TP391.4; 为了解决三七叶片密集病害和小区域病害检测不准确的问题,该研究提出了一种改进的YOLOv3(You Only Look Once v3)目标检测算法(AD-YOLOv3)对三七叶片各种病害进行检测.AD-YOLOv3使用注意力特征金字塔(Attention Feature...
SourceID wanfang
SourceType Aggregation Database
StartPage 164
Title 改进YOLOv3算法检测三七叶片病害
URI https://d.wanfangdata.com.cn/periodical/nygcxb202203019
Volume 38
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LbxMxELbaVEJwQDzFWzngC1WCd7322kc72ahCiF5aqZyq7CPpKUhtiqAnJIRQxYELqAeO3HupQOXEf0Ftws9gxnGSTamAcllZszOezzNxZrw7axNyP87TOAsLUcvwTWuEbwpVRxW1OEvzsJ0W-HIQqy2eyKXV6NGaWJub_1GqWtrup_Vs59TvSv7Hq0ADv-JXsmfw7KRTIEAb_AtX8DBc_8nHNJFUR9RqmihqW1Tbp8uPl59zmsTUJFTHyGA51QIbhmNZA1IEVZYmIKio0uMGSAG9Ra1EcaCrGBvQiRJ4Czq0vJzLOn4A0PA9aIMUC6qFA6CcFhBsUiNRrzJwa-xhh106JaNGY-aOdaOCfi3kum540IueYWmiCuhfM2payAKYTWvKAlga1DAHMxhr9g84YG3MZopFSuYQaDITOOgRNXFpDLEzt7OmZdj2UokfBBATZ0pAizwGwU3FNdUAMYSftTco2g4EQaSxGEWc4ZHZYkyS40biFDNHmRj4NHDAiQic1Qw_o3_K4E4CWIQMWzBWimAY4qTycciHOK5KU5mX4lUw2kLepz7B6BSl36OqjoULq6ihPtFQR2-5LYK9ttmNy3svu9mLFHlw2a3nyUIIS7mwQhaMbdrWNGkP8LnEJKqEuDeDnC6CRcDxCIZJ4RaWLQhXw-BhnCN0DPLhnyC6z_J6nXavW8ogVy6Ri37pVzWjeXyZzO1sXCEXTHfTb39TXCUPBh--_fz-aTSLh_t7g4OPg8-vBl_eHR3uHh2-Pn7_dbj7drj35nj_4BpZbSUrjaWaP82kthVgjVms85B1orCdt3mo4jgNC0zHIV9WnRR3LpQsiDppELF2obXItJQFS1nEO5AxCpbz66TSe9YrbpBqnuM2fOAtWO5BQh61eQbrjhBisZZ5xuVNUvUjXff_VlvrJ7xx6-8st8n56XS8Qyr9ze3iLmTg_fSed-EvvkeTyQ
linkProvider EBSCOhost
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E6%94%B9%E8%BF%9BYOLOv3%E7%AE%97%E6%B3%95%E6%A3%80%E6%B5%8B%E4%B8%89%E4%B8%83%E5%8F%B6%E7%89%87%E7%97%85%E5%AE%B3&rft.jtitle=%E5%86%9C%E4%B8%9A%E5%B7%A5%E7%A8%8B%E5%AD%A6%E6%8A%A5&rft.au=%E6%96%87%E6%96%8C&rft.au=%E6%9B%B9%E4%BB%81%E8%BD%A9&rft.au=%E6%9D%A8%E5%90%AF%E8%89%AF&rft.au=%E5%BC%A0%E5%81%A5&rft.date=2022-02-01&rft.pub=%E4%B8%89%E5%B3%A1%E5%A4%A7%E5%AD%A6%E7%94%B5%E6%B0%94%E4%B8%8E%E6%96%B0%E8%83%BD%E6%BA%90%E5%AD%A6%E9%99%A2%2C%E5%AE%9C%E6%98%8C+443000%25%E6%98%86%E6%98%8E%E7%90%86%E5%B7%A5%E5%A4%A7%E5%AD%A6%E7%8E%B0%E4%BB%A3%E5%86%9C%E4%B8%9A%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E6%98%86%E6%98%8E+650500&rft.issn=1002-6819&rft.volume=38&rft.issue=3&rft.spage=164&rft.epage=172&rft_id=info:doi/10.11975%2Fj.issn.1002-6819.2022.03.019&rft.externalDocID=nygcxb202203019
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fnygcxb%2Fnygcxb.jpg