A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
IEEE Wireless Communications, vol. 30, pp. 76–81
Abstract
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.
Authors 9
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Beijing University of Technology
Affiliation as printed
Beijing University of Technology,China
Beijing University of Technology, China
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China Academy of Transportation Sciences
Affiliation as printed
China Academy of Transportation Science,China
China Academy of Transportation Science, China
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Beijing Technology and Business University
Affiliation as printed
Beijing Technology and Business University,China
Beijing Technology and Business University, China
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Affiliation as printed
Yangzhou University,China
Yangzhou University, China
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Affiliation as printed
Swansea University,UK
Swansea University, UK
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Beijing University of Technology
Affiliation as printed
Beijing University of Technology,China
Beijing University of Technology, China
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Affiliation as printed
City University of Hong Kong,China
City University of Hong Kong, China
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Affiliation as printed
Qingdao Yicheng Sichuang Link of Things Technology Co., LTD,Chin
Qingdao Yicheng Sichuang Link of Things Technology Co., LTD, Chin
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Beijing University of Technology · Swansea University
Affiliation as printed
Swansea University,UK
Beijing University of Technology, China
Swansea University, UK
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