@article{ART002813414},
author={LIYUJIE and Kang Sun-Kyung and Jung， Sung-Tae},
title={Real-time Segmentation of Black Ice Region in Infrared Road Images},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2022},
volume={27},
number={2},
pages={33-42},
doi={10.9708/jksci.2022.27.02.033}
TY - JOUR
AU - LIYUJIE
AU - Kang Sun-Kyung
AU - Jung， Sung-Tae
TI - Real-time Segmentation of Black Ice Region in Infrared Road Images
JO - Journal of The Korea Society of Computer and Information
PY - 2022
VL - 27
IS - 2
PB - The Korean Society Of Computer And Information
SP - 33
EP - 42
SN - 1598-849X
AB - In this paper, we proposed a deep learning model based on multi-scale dilated convolution feature fusion for the segmentation of black ice region in road image to send black ice warning to drivers in real time.
In the proposed multi-scale dilated convolution feature fusion network, different dilated ratio convolutions are connected in parallel in the encoder blocks, and different dilated ratios are used in different resolution feature maps, and multi-layer feature information are fused together. The multi-scale dilated convolution feature fusion improves the performance by diversifying and expending the receptive field of the network and by preserving detailed space information and enhancing the effectiveness of diated convolutions. The performance of the proposed network model was gradually improved with the increase of the number of dilated convolution branch. The mIoU value of the proposed method is 96.46%, which was higher than the existing networks such as U-Net, FCN, PSPNet, ENet, LinkNet. The parameter was 1,858K, which was 6 times smaller than the existing LinkNet model. From the experimental results of Jetson Nano, the FPS of the proposed method was 3.63, which can realize segmentation of black ice field in real time.
KW - Black Ice;Image Segmentation;Dilated Convolution;Receptive Field
DO - 10.9708/jksci.2022.27.02.033
ER -
LIYUJIE, Kang Sun-Kyung and Jung， Sung-Tae. (2022). Real-time Segmentation of Black Ice Region in Infrared Road Images. Journal of The Korea Society of Computer and Information, 27(2), 33-42.
LIYUJIE, Kang Sun-Kyung and Jung， Sung-Tae. 2022, "Real-time Segmentation of Black Ice Region in Infrared Road Images", Journal of The Korea Society of Computer and Information, vol.27, no.2 pp.33-42. Available from: doi:10.9708/jksci.2022.27.02.033
LIYUJIE, Kang Sun-Kyung, Jung， Sung-Tae "Real-time Segmentation of Black Ice Region in Infrared Road Images" Journal of The Korea Society of Computer and Information 27.2 pp.33-42 (2022) : 33.
LIYUJIE, Kang Sun-Kyung, Jung， Sung-Tae. Real-time Segmentation of Black Ice Region in Infrared Road Images. 2022; 27(2), 33-42. Available from: doi:10.9708/jksci.2022.27.02.033
LIYUJIE, Kang Sun-Kyung and Jung， Sung-Tae. "Real-time Segmentation of Black Ice Region in Infrared Road Images" Journal of The Korea Society of Computer and Information 27, no.2 (2022) : 33-42.doi: 10.9708/jksci.2022.27.02.033
LIYUJIE; Kang Sun-Kyung; Jung， Sung-Tae. Real-time Segmentation of Black Ice Region in Infrared Road Images. Journal of The Korea Society of Computer and Information, 27(2), 33-42. doi: 10.9708/jksci.2022.27.02.033
LIYUJIE; Kang Sun-Kyung; Jung， Sung-Tae. Real-time Segmentation of Black Ice Region in Infrared Road Images. Journal of The Korea Society of Computer and Information. 2022; 27(2) 33-42. doi: 10.9708/jksci.2022.27.02.033
LIYUJIE, Kang Sun-Kyung, Jung， Sung-Tae. Real-time Segmentation of Black Ice Region in Infrared Road Images. 2022; 27(2), 33-42. Available from: doi:10.9708/jksci.2022.27.02.033
LIYUJIE, Kang Sun-Kyung and Jung， Sung-Tae. "Real-time Segmentation of Black Ice Region in Infrared Road Images" Journal of The Korea Society of Computer and Information 27, no.2 (2022) : 33-42.doi: 10.9708/jksci.2022.27.02.033
