The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Pembahagian pucuk teh melalui penglihatan mesin ialah teknologi teras pemetik teh automatik. Kaedah baru untuk Segmentasi Pucuk Teh berdasarkan Rangkaian penyahkod-pengekod konvolusi dalam yang dipertingkatkan (TS-SegNet) dicadangkan dalam kertas kerja ini. Untuk meningkatkan ketepatan dan kestabilan pembahagian, penambahbaikan dilakukan oleh fungsi kehilangan pusat kontras dan sambungan langkau. Oleh itu, kekompakan dalam kelas dan kebolehpisahan antara kelas digunakan secara menyeluruh, dan TS-SegNet boleh memperoleh ciri pucuk teh yang lebih diskriminatif. Keputusan eksperimen menunjukkan bahawa kaedah yang dicadangkan membawa kepada hasil pembahagian yang baik, dan pucuk teh yang disegmen hampir bertepatan dengan kebenaran tanah.
Chunhua QIAN
Nanjing Forestry University,Suzhou Polytechnic Institute of Agriculture, Suzhou
Mingyang LI
Nanjing Forestry University
Yi REN
Suzhou Polytechnic Institute of Agriculture, Suzhou
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Salinan
Chunhua QIAN, Mingyang LI, Yi REN, "Tea Sprouts Segmentation via Improved Deep Convolutional Encoder-Decoder Network" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 2, pp. 476-479, February 2020, doi: 10.1587/transinf.2019EDL8147.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDL8147/_p
Salinan
@ARTICLE{e103-d_2_476,
author={Chunhua QIAN, Mingyang LI, Yi REN, },
journal={IEICE TRANSACTIONS on Information},
title={Tea Sprouts Segmentation via Improved Deep Convolutional Encoder-Decoder Network},
year={2020},
volume={E103-D},
number={2},
pages={476-479},
abstract={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.},
keywords={},
doi={10.1587/transinf.2019EDL8147},
ISSN={1745-1361},
month={February},}
Salinan
TY - JOUR
TI - Tea Sprouts Segmentation via Improved Deep Convolutional Encoder-Decoder Network
T2 - IEICE TRANSACTIONS on Information
SP - 476
EP - 479
AU - Chunhua QIAN
AU - Mingyang LI
AU - Yi REN
PY - 2020
DO - 10.1587/transinf.2019EDL8147
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2020
AB - 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.
ER -