Automatic pear and apple detection by videos using deep learning and a Kalman filter
Automatic pear and apple detection by videos using deep learning and a Kalman filter
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DOI：
10.1364/osac.424583
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发表时间：
2021-05-15
期刊：
影响因子：
1.6
通讯作者：
Hosoi, Fumiki
中科院分区：
文献类型：
--
作者：
Itakura, Kenta;Narita, Yuma;Hosoi, Fumiki
Pears and apples in videos recorded while walking were detected automatically using a deep-learning-based method referred to as YOLO. The same fruits in the successive video frames were then identified using a Kalman filter. The average precision of the pear detection was 0.97, while the number of correctly counted pears was 226, out of 234. A YOLO v2 network with a larger input image size and data augmentation method contributed to the high accuracy in the counting. The pears and apples in the videos were counted automatically, within an absolute error of 10% under unstable light conditions and with greenish fruits. (C) 2021 Optical Society of America under the terms of the OSA Open Access Publishing Agreement
