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A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
IEEE Wireless Communications, Volume: 30, Issue: 2, Pages: 76 - 81
Swansea University Author: Yue Hou
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Crack recognition is important in periodic pavement inspection and maintenance. The wide application of image recognition technology in daily inspection and maintenance makes the health monitoring of asphalt pavement defects more effective, both intelligently and sustainably. In this study, a mobile...
|Published in:||IEEE Wireless Communications|
Institute of Electrical and Electronics Engineers (IEEE)
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Crack recognition is important in periodic pavement inspection and maintenance. The wide application of image recognition technology in daily inspection and maintenance makes the health monitoring of asphalt pavement defects more effective, both intelligently and sustainably. In this study, a mobile automatic system integrating fifth-generation wireless communication technology (5G), cloud computing, and artificial intelligence (AI) was proposed for transportation infrastructure object recognition. The original dataset contained 344 images of pavement defects, including longitudinal cracks, transverse cracks, alligator cracks, and broken road markings. Three lightweight algorithms for automatic pavement crack identification were used and compared, including MobileNetV2, ShuffleNetV2, and Res-Net50 networks, respectively. The results showed that the model based on ShuffieNetV2 achieved the best overall predictive accuracy (ACC = 95.52 percent). A mobile automatic monitoring system based on the cloud platform and Android framework was then established. With the help of 5G technology, the cloud-network-terminal’ interconnection can be achieved to provide fast and stable information transmission between transportation infrastructure and road users. The proposed system provides an engineering reference for the transportation infrastructure inspection and maintenance using the 5G communication technology.
Faculty of Science and Engineering
This work was supported by Key Science and Technology Projects in the Transportation Industry in 2021 (2021-ZD2-047), Plan Project of Shandong Transportation S&T (2021B49), Natural Science Foundation of Heilongjiang Province of China (JJ2020ZD0015), and Opening Project Fund of Materials Service Safety Assessment Facilities (MSAF-2021-005). The authors would like to express sincere gratitude to Prof. Xingyu Gu for sharing the data.