Abstract
Road surface condition (RSC) is an important indicator for road maintenance departments to survey, inspect, clean, and repair roads. The number of traffic accidents can increase dramatically in winter or during seasonal changes when extreme weather often occurs. To achieve real-time and automatic RSC monitoring, this paper first proposes an improved Mask-RCNN model based on Swin Transformer and path aggregation feature pyramid network (PAFPN) as the backbone network. A dynamic head is then adopted as the detection network. Meanwhile, transfer learning is used to reduce training time, and data enhancement and multiscale training are applied to achieve better performance. In the first experiment, a real-world RSC dataset collected from Ministry of Transportation Ontario, Canada is used, and the testing result show that the reidentification accuracy of the proposed model is superior to that of other popular methods, such as traditional Mask-RCNN, RetinaNet, Swin Double head RCNN, and Cascade Swin-RCNN, in terms of recognition accuracy and training speed. Moreover, this paper also designs a second experiment and proved that the proposed model can accurately detect road surface areas when light condition is poor, such as night time in extreme weather.
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Acknowledgements
This research is supported by National Natural Science Foundation of China under grant no. 62103177, Shandong Provincial Natural Science Foundation for Youth of China under grant no. ZR2023QF097 and the National Science and Engineering Research Council of Canada (NSERC) under grant no. 651247. The authors would also like to thank the Ministry of Transportation Ontario Canada for technical support.
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Pan, G., Bai, Z., Fu, L., Zhao, L., Xiao, Q. (2024). Road Meteorological State Recognition in Extreme Weather Based on an Improved Mask-RCNN. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1968. Springer, Singapore. https://doi.org/10.1007/978-981-99-8181-6_1
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