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http://dx.doi.org/10.6109/jkiice.2021.25.12.1835

A Black Ice Recognition in Infrared Road Images Using Improved Lightweight Model Based on MobileNetV2  

Li, Yu-Jie (School of Intelligent Manufacturing, Weifang University of Science and Technology)
Kang, Sun-Kyoung (Department of Computer Software Engineering, Wonkwang University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1835-1845 More about this Journal
Abstract
To accurately identify black ice and warn the drivers of information in advance so they can control speed and take preventive measures. In this paper, we propose a lightweight black ice detection network based on infrared road images. A black ice recognition network model based on CNN transfer learning has been developed. Additionally, to further improve the accuracy of black ice recognition, an enhanced lightweight network based on MobileNetV2 has been developed. To reduce the amount of calculation, linear bottlenecks and inverse residuals was used, and four bottleneck groups were used. At the same time, to improve the recognition rate of the model, each bottleneck group was connected to a 3×3 convolutional layer to enhance regional feature extraction and increase the number of feature maps. Finally, a black ice recognition experiment was performed on the constructed infrared road black ice dataset. The network model proposed in this paper had an accurate recognition rate of 99.07% for black ice.
Keywords
Black ice recognition; Transfer learning; Infrared images; MobileNetV2; Lightweight;
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