The traditional tea bud detection method based on machine vision has the problems of poor robustness and low accuracy in manual feature extraction. The target detection algorithm YOLO based on deep learning is applied to the detection of tea tender image under complex background for the first time, and the architecture of YOLO network is improved from the aspect of multi-scale detection, using large-scale and mesoscale detection instead of the original multi-scale detection. In the preprocessing stage, the image of tea shoots under complex background is segmented by combining the super green feature and the OSTU algorithm, which makes the tea tender bud area more obvious. The experimental results show that, by comparing with other algorithms, the target detection algorithm based on deep learning has a high detection precision for the tea tender shoots under complex background, which provides a basis for the research of intelligent picking equipment for tea tender shoots under complex background.
SUN Xiaoxiao
,
MU Shaomin
,
XU Yongyu
,
CAO Zhihao
,
SU Tingting
. Detection algorithm of tea tender buds under complex background based on deep learning[J]. Journal of Hebei University(Natural Science Edition), 2019
, 39(2)
: 211
-216
.
DOI: 10.3969/j.issn.1000-1565.2019.02.015
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