Skip to main content
SpringerLink
Log in

Feature extraction using Hough transform for solid waste bin level detection and classification

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

This paper deals with the solid waste image detection and classification to detect and classify the solid waste bin level. To do so, Hough transform techniques is used for feature extraction to identify the line detection based on image’s gradient field. The feedforward neural network (FFNN) model is used to classify the level content of solid waste based on learning concept. Numbers of training have been performed using FFNN to learn and match the targets of the testing images to compute the sum squared error with the performance goal met. The images for each class are used as input samples for classification. Result from the neural network and the rules decision are used to build the receiver operating characteristic (ROC) graph. Decision graph shows the performance of the system waste system based on area under curve (AUC), WS-class reached 0.9875 for excellent result and WS-grade reached 0.8293 for good result. The system has been successfully designated with the motivation of solid waste bin monitoring system that can applied to a wide variety of local municipal authorities system.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  • Abdoli, S. (2009). RFID application in municipal solid waste management system. International Journal of Environmental Research, 3(3), 447–454.

    Google Scholar 

  • Alam Flora (2009). Towards a clean environment, proposal on sustainable and integrated solid waste management system for University Kebangsaan Malaysia.

  • Arebey, M., Hannan, M. A., Hassan, B., Begum, R. A., & Huda, A. (2011). Integrated technologies for solid waste bin monitoring system. Environmental Monitoring and Assessment, 177(1–4), 399–408.

  • Belal, C., & Morshed, U. C. (2007). RFID-based real-time smart waste management system. Australasian Telecommunication Networks and Application Conference, Christchurch, New Zealand.

  • Dimosthenis, K. (2008). Detecting gradients in text images using the Hough transform. The Eight IAPR International Workshop on Document Analysis Systems, Computer Vision Centre, UAB, Barcelona, Spain.

  • Fadel, A. A. (2006). Study to improve policies for Municipal solid waste in Malaysia. M. S. thesis, Department of Civil and Structural Engineering, Universiti Kebangsaan Malaysia.

  • Hamarneh, G., Althoff, K., & Abu-Gharbieh, R. (1999). Automatic line detection. Project report for the computer vision course. Image Analysis Group, Department of Signals and Systems Chalmers University of Technology.

  • Hannan, M. A., Arebey, M., Begum, R. A., & Basri, H. (2009). Radio frequency identification (RFID) and communication technologies for solid waste bin and truck monitoring system. Waste Management, 31(12), 2406–2413.

  • Hassan, M. N., Chong, T. L., Rahman, M. M., Salleh, M. N., Zakariah, Z., & Awang, M. (2000). Solid waste management-what’s the Malaysian position. Seminar waste to energy, Universiti Putra Malaysia.

  • JICA. (2010). Annual report: The study on national waste minimization in Malaysia. Japan International Cooperation Agency. http://www.kpkt.gov.my/jpspn/fileupload/Laporan/GEJR06042_Summary.pdf. Accessed 09 July 2013.

  • Johansson, O. M. (2006). The effect of dynamic scheduling and routing in a solid waste management system. Waste Management, 26, 875–885.

  • Latifah, A. M., Mohd, A. A. S., & NurIlyana, M. Z. (2009). Municipal solid waste management in Malaysia: practices and challenges. Waste Management, 29(2009), 2902–2906.

    Google Scholar 

  • Lee, S.-J., Ahn, H., Cho, H.-J., Lee, J.-H., & Rhee, S.-B. (2008). Image gradient detection with Hough transform. International Conference on Convergence and Hybrid Information Technology, 2008, 753–756.

  • McAndrew, A. (2004). Introduction to digital image processing with Matlab. Boston: Thomson Course Technology.

    Google Scholar 

  • Ping, L. I., & Yang, S. H. (2006). Integrating GIS with the GeoEnviron database system as a robust tool for integrated solid waste management in Malaysia. 6th Annual Conference and Exibition on geographic information technology and application. Map Asia. http://www.ecoideal.com.my/downloads/PaperMapAsia.pdf. Accessed 15 July 2013.

  • Rafia, A., & Masuda, M. M. (2010). Using a contingent valuation approach for improved solid waste management facility: evidence from Kuala Lumpur. Malaysia: A Department of Economics, Faculty of Economics and Management Science, International Islamic University Malaysia, Malaysia.

    Google Scholar 

  • Rau, J. Y., & Chen, L. C. (2003). Fast straight lines detection using Hough transform with principal axis analysis. Journal of Photogrammetry and Remote Sensing, 8(1), 15–34.

    Google Scholar 

  • Richard, C. D. (2006). Circuits, signals, and speech and image processing: the electrical engineering handbook. (3rd ed.). Boca Raton: Taylor and Francis.

  • Rovetta, A., Fan, X., Vicentini, F., Minghua, Z., Giusti, A., & He, Q. (2009). Early detection and evaluation of waste through sensorized containers for a collection monitoring application. Waste Management, 29(2009), 2939–2949.

    Article  Google Scholar 

  • Russell, S., & Norvig, P. (2003). Artificial intelligence a modern approach (2nd ed.). New Jersey: Prentice Hall Series in Artificial Intelligence.

    Google Scholar 

  • Shylaja, S. S., Balasubramanya Murthy, K. N., Natarajan Nischitch, S., Muthuraj, R., & Ajay, S. (2011). Feed forward neural network based eye localization and recognition using Hough transform. International Journal of Advanced Computer Science and Applications, 2(3), 104–109.

    Google Scholar 

  • The World Bank, East Asia Infrastructure Department (EAID) (2005). Waste management in China: issues and recommendations. Urban Development Working Paper No.9

Download references

Acknowledgments

The authors are acknowledged this work for the financial support under the grant of Ministry of Higher Education (MOHE) as LRGS/TD/2011/UKM/ICT/04/01 and PRGS/1/12/TK02/UKM/2012.

Author information

Authors and Affiliations

  1. Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia

    M. A. Hannan, W. A. Zaila, M. Arebey & H. Basri

  2. Institute of Climate Change, Universiti Kebangsaan Malaysia, Bangi, 43600, Selangor, Malaysia

    R. A. Begum

Authors
  1. M. A. Hannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. W. A. Zaila
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. Arebey
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. R. A. Begum
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. H. Basri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. A. Hannan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hannan, M.A., Zaila, W.A., Arebey, M. et al. Feature extraction using Hough transform for solid waste bin level detection and classification. Environ Monit Assess 186, 5381–5391 (2014). https://doi.org/10.1007/s10661-014-3786-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10661-014-3786-6

Keywords

Search

Navigation