Knowledge Commons of Institute of Automation,CAS
Apple detection during different growth stages in orchards using the improved YOLO-V3 model | |
Tian, Yunong; Yang, Guodong; Wang, Zhe; Wang, Hao; Li, En1; Liang, Zize | |
发表期刊 | COMPUTERS AND ELECTRONICS IN AGRICULTURE |
ISSN | 0168-1699 |
2019-02-01 | |
卷号 | 157页码:417-426 |
通讯作者 | Li, En(en.li@ia.ac.cn) |
摘要 | Real-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304 s per frame at 3000 x 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards. |
关键词 | Apple images acquisition Image augmentation Deep learning YOLOV3-dense Real-time detection |
DOI | 10.1016/j.compag.2019.01.012 |
关键词[WOS] | VISION ; FRUITS |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2017YFD0701401] ; 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 ; Computer Science |
WOS类目 | Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications |
WOS记录号 | WOS:000459358400041 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 多模态智能 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/25015 |
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, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China |
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. Apple detection during different growth stages in orchards using the improved YOLO-V3 model[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2019,157:417-426. |
APA | Tian, Yunong,Yang, Guodong,Wang, Zhe,Wang, Hao,Li, En,&Liang, Zize.(2019).Apple detection during different growth stages in orchards using the improved YOLO-V3 model.COMPUTERS AND ELECTRONICS IN AGRICULTURE,157,417-426. |
MLA | Tian, Yunong,et al."Apple detection during different growth stages in orchards using the improved YOLO-V3 model".COMPUTERS AND ELECTRONICS IN AGRICULTURE 157(2019):417-426. |
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