A Method Based on Data Fusion of Multiple Sensors to Evaluate Road Cleanliness

  • Xiang Yao
  • Wei Zhang
  • Wei Cui
  • Xu ZhangEmail author
  • Ying Wang
  • Jiale Xiong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11741)


For the supervision and evaluation of the quality of garbage detection and sanitation cleaning of urban roads, it has been carried out on a road-by road inspection by manual means for a long time, and then the mobile phone is used to locate the garbage and quantify the score. However, there are many problems in manual supervision. Not only is the work efficiency very low, but also a lot of manpower and material resources are wasted. This has not been able to meet the current smart city governance needs. Therefore, we propose a method of integrating multi-sensor data to replace the manual detection and evaluation of road cleanliness automatically and intelligently, and design a complete system, which can be loaded on the car to work on the road efficiently and directly output the score. Experiments show that our method can replace manual well to realize the identification and classification of garbage on the road, the calculation of garbage area, and the calculation of the latitude and longitude of the garbage, then display the above information of garbage in real time on the web map side.


Road sanitation assessment Data fusion Smart system 



This research was partially supported by the National Nature Science Foundation of China (Grant no. 51575332) and the key research project of Ministry of science and technology ((Grant no. 2017YFB1301503 and no. 2018YFB1306802).


  1. 1.
    Zhuang, C.: Thoughts on the long-term supervision mechanism of sanitation and cleaning marketization. China Constr. Inf. 24, 75–76 (2014)Google Scholar
  2. 2.
    Sun, J.: Problems and countermeasures of market operation supervision of sanitation roads. Big Technol. 3(7), 308 (2016)Google Scholar
  3. 3.
    Wei, Z.: Discussion on the Market Supervision Mechanism of Environmental Sanitation Cleaning. Residential and Real Estate. 7(3), 141,231 (2017)Google Scholar
  4. 4.
    Mittal, G., Yagnik, K.B., Garg, M., et al.: Spot Garbage: smart phone app to detect garbage using deep learning. In: ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 940–945 (2016)Google Scholar
  5. 5.
    Wei, S., Cheng, Z.: Image-based automatic detection of urban scene garbage. Integr. Technol. 6(01), 39–52 (2017)Google Scholar
  6. 6.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NIPS, pp. 91–99 (2015)Google Scholar
  7. 7.
    Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks (2013). arXiv:1311.2901,2013
  8. 8.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 1–8 (2016)Google Scholar
  9. 9.
    Redmon, J., Farhadi, A.: Yolov3: an incremental improvement (2018). arXiv:1804.02767,2018
  10. 10.
    Qu, S., et al.: Method for measuring height measurement area based on monocular vision ranging. Sci. Technol. Eng. 16(02), 224–228 (2016)Google Scholar
  11. 11.
    Chen, Y.X., Das, M., Bajpai, D.: Vehicle tracking and distance estimation based on multiple image features. IEEE Computer and Robot Vision, pp. 371–378 (2007)Google Scholar
  12. 12.
    You, L., et al.: Application of USB camera parallel binocular vision system in area measurement. Appl. Technol. 02, 1–5 (2008)Google Scholar
  13. 13.
    Liu, L., et al.: Derivation of calculation formula for conversion of GPS coordinates with direction and distance. Southern Agricultural Machinery (2018)Google Scholar
  14. 14.
    Li, Z., Li, J.: Fast calculation of distance between two points and measurement error based on latitude and longitude. Mapp. Spat. Geogr. Inf. 36(11), 235–237 (2013)Google Scholar
  15. 15.
    Kong, X., et al.: Foundation of Geodesy, 2nd edn., pp. 158–179. WuHan University Press, Luojiashan (2005)Google Scholar
  16. 16.
    Zhang, Z.: A flexible new technique for camera calibration. Tpami 22(11), 1330–1334 (2000)CrossRefGoogle Scholar
  17. 17.
    Wu, Y., Hu, Z.: PnP problem revisited. J. Math. Imaging Vis. 24(1), 131–141 (2006)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Xiang Yao
    • 1
    • 3
  • Wei Zhang
    • 2
  • Wei Cui
    • 1
    • 3
  • Xu Zhang
    • 1
    • 3
    Email author
  • Ying Wang
    • 1
  • Jiale Xiong
    • 1
  1. 1.School of Mechatronic Engineering and AutomationShanghai UniversityShanghaiChina
  2. 2.Juli Green Environmental Technology Research Center of ShanghaiShanghaiChina
  3. 3.HUST-Wuxi Research InstituteWuxiChina

Personalised recommendations