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Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3

Bibliographic Details
Main Author: Si, Haiping
Publication Date: 2023
Other Authors: Wang, Yunpeng, Zhao, Wenrui, Wang, Ming, Song, Jiazhen, Wan, Li, Song, Zhengdao, Li, Yujie, Bação, Fernando, Sun, Changxia
Format: Article
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/152534
Summary: Si, H., Wang, Y., Zhao, W., Wang, M., Song, J., Wan, L., Song, Z., Li, Y., Bação, F., & Sun, C. (2023). Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3. Agriculture (Switzerland), 13(4), 1-26. [824]. https://doi.org/10.3390/agriculture13040824---This research is funded by the Henan Province Key Science-Technology Research Project under Grant No. 232102520006, the National Science and Technology Resource Sharing Service Platform Project under Grant No. NCGRC-2020-57.
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spelling Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3deep learningdefect detectionimage fusiontransfer learningweight comparisonWC-MobileNetV3Food ScienceAgronomy and Crop SciencePlant ScienceSi, H., Wang, Y., Zhao, W., Wang, M., Song, J., Wan, L., Song, Z., Li, Y., Bação, F., & Sun, C. (2023). Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3. Agriculture (Switzerland), 13(4), 1-26. [824]. https://doi.org/10.3390/agriculture13040824---This research is funded by the Henan Province Key Science-Technology Research Project under Grant No. 232102520006, the National Science and Technology Resource Sharing Service Platform Project under Grant No. NCGRC-2020-57.Apples are ranked third, after bananas and oranges, in global fruit production. Fresh apples are more likely to be appreciated by consumers during the marketing process. However, apples inevitably suffer mechanical damage during transport, which can affect their economic performance. Therefore, the timely detection of apples with surface defects can effectively reduce economic losses. In this paper, we propose an apple surface defect detection method based on weight contrast transfer and the MobileNetV3 model. By means of an acquisition device, a thermal, infrared, and visible apple surface defect dataset is constructed. In addition, a model training strategy for weight contrast transfer is proposed in this paper. The MobileNetV3 model with weight comparison transfer (Weight Compare-MobileNetV3, WC-MobileNetV3) showed a 16% improvement in accuracy, 14.68% improvement in precision, 14.4% improvement in recall, and 15.39% improvement in F1-score. WC-MobileNetV3 compared to MobileNetV3 with fine-tuning improved accuracy by 2.4%, precision by 2.67%, recall by 2.42% and F1-score by 2.56% compared to the classical neural networks AlexNet, ResNet50, DenseNet169, and EfficientNetV2. The experimental results show that the WC-MobileNetV3 model adequately balances accuracy and detection time and achieves better performance. In summary, the proposed method achieves high accuracy for apple surface defect detection and can meet the demand of online apple grading.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNSi, HaipingWang, YunpengZhao, WenruiWang, MingSong, JiazhenWan, LiSong, ZhengdaoLi, YujieBação, FernandoSun, Changxia2023-05-08T22:11:08Z2023-04-032023-04-03T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article26application/pdfhttp://hdl.handle.net/10362/152534eng2077-0472PURE: 59939027https://doi.org/10.3390/agriculture13040824info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T18:11:15Zoai:run.unl.pt:10362/152534Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T17:41:30.564928Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
title Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
spellingShingle Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
Si, Haiping
deep learning
defect detection
image fusion
transfer learning
weight comparison
WC-MobileNetV3
Food Science
Agronomy and Crop Science
Plant Science
title_short Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
title_full Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
title_fullStr Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
title_full_unstemmed Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
title_sort Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3
author Si, Haiping
author_facet Si, Haiping
Wang, Yunpeng
Zhao, Wenrui
Wang, Ming
Song, Jiazhen
Wan, Li
Song, Zhengdao
Li, Yujie
Bação, Fernando
Sun, Changxia
author_role author
author2 Wang, Yunpeng
Zhao, Wenrui
Wang, Ming
Song, Jiazhen
Wan, Li
Song, Zhengdao
Li, Yujie
Bação, Fernando
Sun, Changxia
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Information Management Research Center (MagIC) - NOVA Information Management School
NOVA Information Management School (NOVA IMS)
RUN
dc.contributor.author.fl_str_mv Si, Haiping
Wang, Yunpeng
Zhao, Wenrui
Wang, Ming
Song, Jiazhen
Wan, Li
Song, Zhengdao
Li, Yujie
Bação, Fernando
Sun, Changxia
dc.subject.por.fl_str_mv deep learning
defect detection
image fusion
transfer learning
weight comparison
WC-MobileNetV3
Food Science
Agronomy and Crop Science
Plant Science
topic deep learning
defect detection
image fusion
transfer learning
weight comparison
WC-MobileNetV3
Food Science
Agronomy and Crop Science
Plant Science
description Si, H., Wang, Y., Zhao, W., Wang, M., Song, J., Wan, L., Song, Z., Li, Y., Bação, F., & Sun, C. (2023). Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3. Agriculture (Switzerland), 13(4), 1-26. [824]. https://doi.org/10.3390/agriculture13040824---This research is funded by the Henan Province Key Science-Technology Research Project under Grant No. 232102520006, the National Science and Technology Resource Sharing Service Platform Project under Grant No. NCGRC-2020-57.
publishDate 2023
dc.date.none.fl_str_mv 2023-05-08T22:11:08Z
2023-04-03
2023-04-03T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/152534
url http://hdl.handle.net/10362/152534
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2077-0472
PURE: 59939027
https://doi.org/10.3390/agriculture13040824
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 26
application/pdf
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
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