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http://dx.doi.org/10.9708/jksci.2022.27.02.033

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

Li, Yu-Jie (School of Compute Science, Weifang University of Science and Technology, Dept. of Computer and Software Engineering, Wonkwang University)
Kang, Sun-Kyoung (Dept. of Computer and Software Engineering, Wonkwang University)
Jung, Sung-Tae (Dept. of Computer and Software Engineering, Wonkwang University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.2, 2022 , pp. 33-42 More about this Journal
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.
Keywords
Black Ice; Image Segmentation; Dilated Convolution; Receptive Field;
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Times Cited By KSCI : 2  (Citation Analysis)
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