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http://dx.doi.org/10.30693/SMJ.2020.9.2.9

An Instance Segmentation using Object Center Masks  

Lee, Jong Hyeok (전북대학교 전자.정보공학부)
Kim, Hyong Suk (전북대학교 지능형로봇연구소)
Publication Information
Smart Media Journal / v.9, no.2, 2020 , pp. 9-15 More about this Journal
Abstract
In this paper, we propose a network model composed of Multi path Encoder-Decoder branches that can recognize each instance from the image. The network has two branches, Dot branch and Segmentation branch for finding the center point of each instance and for recognizing area of the instance, respectively. In the experiment, the CVPPP dataset was studied to distinguish leaves from each other, and the center point detection branch(Dot branch) found the center points of each leaf, and the object segmentation branch(Segmentation branch) finally predicted the pixel area of each leaf corresponding to each center point. In the existing segmentation methods, there were problems of finding various sizes and positions of anchor boxes (N > 1k) for checking objects. Also, there were difficulties of estimating the number of undefined instances per image. In the proposed network, an effective method finding instances based on their center points is proposed.
Keywords
neural network; instance segmentation; center detection; object detection;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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1 CVPPP 2017 LSC TRAINING DATASET, https://www.plant-phenotyping.org/CVPPP2019 (accessed Dec., 2019).
2 Hei Law, Jia Deng, "CornerNet: Detecting Objects as Paired Keypoints," ECCV, 2018.
3 T. Lin, P. Goyal, R. Girshick, K. He and P. Dollar, "Focal Loss for Dense Object Detection," 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2999-3007, Venice, 2017.
4 K. He, G. Gkioxari, P. Dollr, and R. Girshick. "Mask r-cnn," 2017 IEEE International Conference on Computer Vision (ICCV), pp. 2980-2988, Oct 2017.
5 Joseph Redmon and Ali Farhadi. "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.
6 X. Zhou, J. Zhuo, and P. Krahenb uhl. "Bottom-up object detection by grouping extreme and center points," CoRR, abs/1901.08043, 2019.
7 Xingyi Zhou, Dequan Wang, and Philipp Krahenbuhl. "Objects as points," arXiv preprint arXiv:1904.07850, 2019.
8 Long, J., Shelhamer, E., and Darrell, T. "Fully convolutional networks for semantic segmentation," CoRR, abs/1411.4038, 2014.
9 Badrinarayanan. V, Kendall. A, and Cipolla. R. "SegNet: A deep convolutional encoder-decoder architecture for image segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 39, pp. 2481-2495, 2017.   DOI
10 O. Ronneberger, P. Fischer and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234-241, 2015.
11 B. D. Brabandere, D. Neven and L. V. Gool, "Semantic Instance Segmentation with a Discriminative Loss Function," CoRR, abs/1708.02551, 2017.
12 Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. "Faster R-CNN: Towards real-time object detection with region proposal networks," IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) , 2017.
13 Lin, T.Y., Goyal, P., Girshick, R., He, K., Doll'ar, P, "Focal loss for dense object detection," arXiv preprint arXiv:1708.02002, 2017.
14 D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," CoRR, abs/1412.6980, 2014.
15 Bernardino Romera-Paredes and Philip Hilaire Sean Torr, "Recurrent instance segmentation," European Conference on Computer Vision (ECCV), pp. 312-329, 2016.
16 Jean-Michel Pape and Christian Klukas, "3-d histogram-based segmentation and leaf detection for rosette plants," European Conference on Computer Vision (ECCV), pp. 61-74, 2014.
17 Mengye Ren and Richard S Zemel, "End-to-end instance segmentation with recurrent attention," 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21-26, Honolulu, HI, USA, 2017.
18 Daniel Ward, Peyman Moghadam, and Nicolas Hudson. "Deep leaf segmentation using synthetic data," CVPPP 2018, Newcastle, UK, Sept. 2018.
19 김서정, 이재수, 김형석, "딥러닝을 이용한 양파밭의 잡초 검출 연구," 스마트미디어저널, 제7권, 제3호, 16-21쪽, 2018년 9월   DOI
20 김서정, 김형석, "Multi-Tasking U-net 기반 파프리카 병해충 진단," 스마트미디어저널, 제9권 제1호, 16-22쪽, 2020년 03월   DOI
21 이한솔, 김영관, 홍지만, "사물인식을 위한 딥러닝 모델 선정 플랫폼," 스마트미디어저널, 제8권, 제2호, 66-73쪽, 2019년 06월   DOI
22 Bert De Brabandere, Davy Neven, and Luc Van Gool, "Semantic instance segmentation with a discriminative loss function," arXiv preprint arXiv:1708.02551, 2017.