Urban Road Water Recognition Based on Deep Learning

  • Xuefeng Tan
  • Mantao WangEmail author
  • Linchao He
  • Mengting Luo
  • Yunlu Lu
  • Jing He
  • Dejun Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 551)


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.


Deep learning Road water Image recognition ResNet50 



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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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Xuefeng Tan
    • 1
  • Mantao Wang
    • 1
    • 2
    Email author
  • Linchao He
    • 1
  • Mengting Luo
    • 1
  • Yunlu Lu
    • 1
  • Jing He
    • 3
  • Dejun Zhang
    • 4
  1. 1.College of Information and EngineeringSichuan Agricultural UniversityYaanChina
  2. 2.The Lab of Agricultural Information Engineering, Sichuan Key LaboratoryYaanChina
  3. 3.College of Information Science and TechnologyChengdu University of TechnologyChengduChina
  4. 4.School of Geography and Information EngineeringChina University of GeosciencesWuhanChina

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