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Proceedings Paper

An efficient FCN based neural network for image semantic segmentation
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Paper Abstract

Image segmentation has always been a key research issue in the field of computer vision. Image segmentation networks that use deep learning methods require a large number of finely labeled samples, which is difficult to obtain. In this paper, we combine the focal loss function with the fully convolutional networks to improve network performance. And we collected and built a dataset contents 1500 samples with complex background. We trained the improved network with the dataset to achieve 81.55% in mean average precision and 76.13% in mean intersection over union.

Paper Details

Date Published: 14 August 2019
PDF: 6 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111794J (14 August 2019); doi: 10.1117/12.2540137
Show Author Affiliations
Ruixin Yang, Beijing Institute of Technology (China)
Chengpo Mu, Beijing Institute of Technology (China)
Yu Yang, Beijing Institute of Technology (China)
Xuejian Li, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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