본문 바로가기
  • Home

Real-time Segmentation of Black Ice Region in Infrared Road Images

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(2), pp.33-42
  • DOI : 10.9708/jksci.2022.27.02.033
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : January 19, 2022
  • Accepted : February 14, 2022
  • Published : February 28, 2022

LIYUJIE 1 Kang Sun-Kyung 1 Jung, Sung-Tae 1

1원광대학교

Accredited

ABSTRACT

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.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.