Tea Sprouts Segmentation via Improved Deep Convolutional Encoder-Decoder Network

Chunhua QIAN  Mingyang LI  Yi REN  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.2   pp.476-479
Publication Date: 2020/02/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDL8147
Type of Manuscript: LETTER
Category: Image Recognition, Computer Vision
tea sprouts segmentation,  TS-SegNet,  skip connections,  contrastive-center loss function,  

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Tea sprouts segmentation via machine vision is the core technology of tea automatic picking. A novel method for Tea Sprouts Segmentation based on improved deep convolutional encoder-decoder Network (TS-SegNet) is proposed in this paper. In order to increase the segmentation accuracy and stability, the improvement is carried out by a contrastive-center loss function and skip connections. Therefore, the intra-class compactness and inter-class separability are comprehensively utilized, and the TS-SegNet can obtain more discriminative tea sprouts features. The experimental results indicate that the proposed method leads to good segmentation results, and the segmented tea sprouts are almost coincident with the ground truth.