TY - JOUR
T1 - A 5G Cloud Platform and Machine Learning-Based Mobile Automatic Recognition of Transportation Infrastructure Objects
AU - Chen, Ning
AU - Shi, Hongyu
AU - Liu, Ruijun
AU - Li, Yujie
AU - Li, Ji
AU - Xu, Zijin
AU - Wang, Dawei
AU - Lu, Guoyang
AU - Jing, Baohong
AU - Hou, Yue
N1 - Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
PY - 2023/4
Y1 - 2023/4
N2 - 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. © 2002-2012 IEEE.
AB - 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. © 2002-2012 IEEE.
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85156097819&origin=recordpage
U2 - 10.1109/MWC.002.2200347
DO - 10.1109/MWC.002.2200347
M3 - RGC 21 - Publication in refereed journal
SN - 1536-1284
VL - 30
SP - 76
EP - 81
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
IS - 2
ER -
