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

文献类型：期刊论文

作者Tian, Yunong1,2; Yang, Guodong1; Wang, Zhe1,2; Li, En1; Liang, Zize1
刊名BIOSYSTEMS ENGINEERING
出版日期2020-05-01
卷号193页码:264-278
关键词Apple flower images acquisition Image augmentation Deep learning MASU R-CNN Instance segmentation
ISSN号1537-5110
DOI10.1016/j.biosystemseng.2020.03.008
通讯作者Li, En(en.li@ia.ac.cn)
英文摘要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.
WOS关键词IMAGE SEGMENTATION ; AGRICULTURE ; ORCHARDS ; VISION
资助项目National Key Research and Development Program of China[2017YFD0701401]
WOS研究方向Agriculture
语种英语
WOS记录号WOS:000526114500021
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
资助机构National Key Research and Development Program of China
源URL[http://ir.ia.ac.cn/handle/173211/38928]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
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
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.

入库方式： OAI收割

来源：自动化研究所

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