Abstract
The urban road water recognition algorithm based on deep learning can achieve the automatic recognition of road water and timely report to relevant departments, thereby eliminating traffic accidents and road congestion caused by road water, and the method effectively reduces hardware resources cost. In this paper, an image recognition algorithm based on ResNet50 is proposed to accomplish the real-time intelligent recognition of urban road water. Firstly, the ResNet50 network is used to extract image features, and then the model training is carried out on the dataset of urban road water. Finally, the trained model is applied to the surveillance video of urban road to recognize the water by optimizing the model parameters. On the dataset of urban road water, the accuracy of the water state recognition of the algorithm is 94.10%, the recognition accuracy of the water level is 59.75%, and the processing speed reaches 15 FPS. The experimental results show that the algorithm has basically achieved real-time recognition of urban road water.
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Acknowledgments
This research was supported in part by the National Natural Science Foundation of China under Grant 61702350, and in part by the Youth Fund of the Sichuan Provincial Education Department under Grant 18ZB0467 and the Lab of Agricultural Information Engineering, Sichuan Key Laboratory.
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Tan, X. et al. (2020). Urban Road Water Recognition Based on Deep Learning. In: Hung, J., Yen, N., Chang, JW. (eds) Frontier Computing. FC 2019. Lecture Notes in Electrical Engineering, vol 551. Springer, Singapore. https://doi.org/10.1007/978-981-15-3250-4_178
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DOI: https://doi.org/10.1007/978-981-15-3250-4_178
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