@inproceedings{be1f561a252945f5bf2d6f07f8c3e38d,
title = "An efficient FCN based neural network for image semantic segmentation",
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.",
keywords = "Convolutional neural network, FCN, Focal loss, Image segmentation",
author = "Ruixin Yang and Chengpo Mu and Yu Yang and Xuejian Li",
note = "Publisher Copyright: {\textcopyright} COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.; 11th International Conference on Digital Image Processing, ICDIP 2019 ; Conference date: 10-05-2019 Through 13-05-2019",
year = "2019",
doi = "10.1117/12.2540137",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jenq-Neng Hwang and Xudong Jiang",
booktitle = "Eleventh International Conference on Digital Image Processing, ICDIP 2019",
address = "United States",
}