Instance segmentation of apple flowers using the improved mask R-CNN model

doi:10.1016/j.biosystemseng.2020.03.008

	Instance segmentation of apple flowers using the improved mask R-CNN model
	Tian, Yunong1,2 ; Yang, Guodong1 ; Wang, Zhe1,2 ; Li, En1 ; Liang, Zize1
发表期刊	BIOSYSTEMS ENGINEERING
ISSN	1537-5110
	2020-05-01
卷号	193 页码:264-278
通讯作者	Li, En(en.li@ia.ac.cn)
摘要	Flower and fruitlet thinning can be an effective method of improving the yield and quality of fruit. Automatic detection flowers and fruits at different growth stages is essential for the intelligent management of apple orchards. The further segmentation of blossom areas contributes to extracting detailed growth information of apple flowers. However, the precise detection and segmentation of blossom images is yet to be fully accomplished. An instance segmentation model which improves Mask Scoring R-CNN with a U-Net backbone (MASU R-CNN) is proposed for the detection and segmentation of apple flowers with three different levels of growth status: bud, semi-open and fully open. The foreground and background of apple flower images were combined based on the growth characteristics of apple flowers. Furthermore, 200 background images were added as background samples to form the image training dataset and a U-Net backbone was used to improve the MaskloU head of Mask Scoring R-CNN model. This method can improve the efficiency of feature utilisation and promote the reuse of features through the concatenation of feature maps in the process of encoding and decoding. The performance of the MASU R-CNN model was verified by 100 testing images. With ResNet-101 FPN adopted as the feature extraction backbone, the precision of MASU R-CNN reached 96.43%, recall 95.37%, F1 score 95.90%, mean average precision (mAP) 0.594, and mean intersection over union (mIoU) 91.55%. The segmentation results of MASU R-CNN model outperformed those of the other state-of-theart models. (C) 2020 IAgrE. Published by Elsevier Ltd. All rights reserved.
关键词	Apple flower images acquisition Image augmentation Deep learning MASU R-CNN Instance segmentation
DOI	10.1016/j.biosystemseng.2020.03.008
关键词[WOS]	IMAGE SEGMENTATION ; AGRICULTURE ; ORCHARDS ; VISION
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFD0701401]
项目资助者	National Key Research and Development Program of China
WOS研究方向	Agriculture
WOS类目	Agricultural Engineering ; Agriculture, Multidisciplinary
WOS记录号	WOS:000526114500021
出版者	ACADEMIC PRESS INC ELSEVIER SCIENCE
七大方向——子方向分类	多模态智能
引用统计	被引频次：76[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/38929
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Li, En
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, 19 A Yuquan Rd, Beijing 100049, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Tian, Yunong,Yang, Guodong,Wang, Zhe,et al. Instance segmentation of apple flowers using the improved mask R-CNN model[J]. BIOSYSTEMS ENGINEERING,2020,193:264-278.
APA	Tian, Yunong,Yang, Guodong,Wang, Zhe,Li, En,&Liang, Zize.(2020).Instance segmentation of apple flowers using the improved mask R-CNN model.BIOSYSTEMS ENGINEERING,193,264-278.
MLA	Tian, Yunong,et al."Instance segmentation of apple flowers using the improved mask R-CNN model".BIOSYSTEMS ENGINEERING 193(2020):264-278.

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