Abnormal area identification of corn ear based on semi-supervised learning

doi:10.1049/ipr2.12492

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Abnormal area identification of corn ear based on semi-supervised learning
	Wei, Jian 1; Ma, Qin 1,2; Wang, Weitao 1; Guo, Hao3 ; Liu, Zhe 3; Zhang, Jiajing4,5
发表期刊	IET IMAGE PROCESSING
ISSN	1751-9659
	2022-04-14
页码	10
通讯作者	Ma, Qin(sockline@163.com)
摘要	Screening corn ear is the key link in the breeding process of new varieties. But manual testing is difficult to measure the proportion of abnormal area. Meanwhile, the abnormal areas are mainly caused by mildew, moth and mechanical collision. A new refined semantic segmentation model was proposed based on the semi-supervised learning method of generating antagonistic networks (GAN). Besides k-means algorithm was used to remove a large amount of background information for data preprocessing. By using feature fusion and weighted loss function the model performance was improved. The introduction of transfer learning accelerated model convergence. Through the high-throughput corn ear collection system, 1448 ear images (including abnormal conditions such as mildew, moth and mechanical damage) were collected and labelled. The proposed method was tested on real corn ear images with an accuracy of 0.950, mean precision of 0.933, mean IoU of 0.884, and FwIoU of 0.908. Experimental results show that the proposed method has better performance than general networks.
DOI	10.1049/ipr2.12492
收录类别	SCI
语种	英语
资助项目	[2021ZD0113701]
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology
WOS记录号	WOS:000782384000001
出版者	WILEY
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48281
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Ma, Qin
作者单位	1.China Agr Univ, Coll Informat & Elect Engn, Beijing 100083, Peoples R China 2.Minist Agr & Rural Affairs, Key Lab Agr Machinery Monitoring & Big Data Appli, Beijing, Peoples R China 3.China Agr Univ, Coll Land Sci & Technol, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wei, Jian,Ma, Qin,Wang, Weitao,et al. Abnormal area identification of corn ear based on semi-supervised learning[J]. IET IMAGE PROCESSING,2022:10.
APA	Wei, Jian,Ma, Qin,Wang, Weitao,Guo, Hao,Liu, Zhe,&Zhang, Jiajing.(2022).Abnormal area identification of corn ear based on semi-supervised learning.IET IMAGE PROCESSING,10.
MLA	Wei, Jian,et al."Abnormal area identification of corn ear based on semi-supervised learning".IET IMAGE PROCESSING (2022):10.