International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
권호 표지
DOI QR코드

DOI QR Code

Sorting for Plastic Bottles Recycling using Machine Vision Methods

  • 투고 : 2024.06.05
  • 발행 : 2024.06.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Due to the increase in population and consequently the increase in the production of plastic waste, recovery of this part of the waste is an undeniable necessity. On the other hand, the recycling of plastic waste, if it is placed in a systematic process and controlled, can be effective in creating jobs and maintaining environmental health. Waste collection in many large cities has become a major problem due to lack of proper planning with increasing waste from population accumulation and changing consumption patterns. Today, waste management is no longer limited to waste collection, but waste collection is one of the important areas of its management, i.e. training, segregation, collection, recycling and processing. In this study, a systematic method based on machine vision for sorting plastic bottles in different colors for recycling purposes will be proposed. In this method, image classification and segmentation techniques were presented to improve the performance of plastic bottle classification. Evaluation of the proposed method and comparison with previous works showed the proper performance of this method.

키워드

참고문헌

  1. Makela, M., Rissanen, M. and Sixta, H., 2020. Machine vision estimates the polyester content in recyclable waste textiles. Resources, Conservation and Recycling, 161, p.105007. 
  2. Zong, Z., Jianli, W. and Xiaofang, R., 2017. Design of standard parts recycling system based on machine vision. Automation & Instrumentation, p.08. 
  3. Wang, C., Hu, Z., Pang, Q. and Hua, L., 2019. Research on the classification algorithm and operation parameters optimization of the system for separating non-ferrous metals from end-of-life vehicles based on machine vision. Waste Management, 100, pp.10-17.  https://doi.org/10.1016/j.wasman.2019.08.043
  4. Asaei, H., Jafari, A. and Loghavi, M., 2019. Site-specific orchard sprayer equipped with machine vision for chemical usage management. Computers and Electronics in Agriculture, 162, pp.431-439.  https://doi.org/10.1016/j.compag.2019.04.040
  5. Asaei, H., Jafari, A. and Loghavi, M., 2019. Site-specific orchard sprayer equipped with machine vision for chemical usage management. Computers and Electronics in Agriculture, 162, pp.431-439.  https://doi.org/10.1016/j.compag.2019.04.040
  6. Laszlo, R., Holonec, R., Copindean, R. and Dragan, F., 2019, May. Sorting system for e-waste recycling using contour vision sensors. In 2019 8th International Conference on Modern Power Systems (MPS) (pp. 1-4). IEEE. 
  7. Huang, H., Moaveni, M., Schmidt, S., Tutumluer, E. and Hart, J.M., 2018. Evaluation of Railway Ballast Permeability Using Machine Vision-Based Degradation Analysis. Transportation Research Record, 2672(10), pp.62-73.  https://doi.org/10.1177/0361198118790849
  8. Bogue, R., 2019. Robots in recycling and disassembly. Industrial Robot: the international journal of robotics research and application. 
  9. Tao, C., Zhang, Y. and Gao, K., 2019. Machine vision analysis on abnormal respiratory conditions of mice inhaling particles containing cadmium. Ecotoxicology and environmental safety, 170, pp.600-610.  https://doi.org/10.1016/j.ecoenv.2018.12.022
  10. Schluter, M., Niebuhr, C., Lehr, J. and Kruger, J., 2018. Vision-based identification service for remanufacturing sorting. Procedia Manufacturing, 21, pp.384-391.  https://doi.org/10.1016/j.promfg.2018.02.135
  11. Liapis S, Tziritas G. Color and texture image retrieval using chromaticity histograms and wavelet frames. IEEE Transactions on Multimedia 2004;6(5):676-86.  https://doi.org/10.1109/TMM.2004.834858
  12. Lin Chuen-Horng, Lin Wei-Chih. Image retrieval system based on adaptive color histogram and texture features. The Computer Journal 2011;54(7):1136-47.  https://doi.org/10.1093/comjnl/bxq066
  13. Chuen-Horng Lin, Huan-Yu Chen, and Y.-S. Wua, Study of image retrieval and classification based on adaptive features using genetic algorithm feature selection. Expert Systems with Applications, 2014. 41(15): p. 6611-6621.  https://doi.org/10.1016/j.eswa.2014.04.033
  14. He Zhenyu, You Xinge, Yuan Yuan. Texture image retrieval based on non-tensor product wavelet filter banks. Signal Processing 2009;89(8):1501-10.  https://doi.org/10.1016/j.sigpro.2009.01.021
  15. Tzagkarakis G, Beferull-Lozano B, Tsakalides P. Rotation-invariant texture retrieval via signature alignment based on Steerable sub-Gaussian modeling. IEEE Transactions on Image Processing 2008;17(7):1212-25.  https://doi.org/10.1109/TIP.2008.924390
  16. Han Ju, Ma Kai-Kuang. Rotation-invariant and scale-invariant Gabor features for texture image retrieval. Image and Vision Computing 2007;25(9):1474-81.  https://doi.org/10.1016/j.imavis.2006.12.015
  17. Nachtegael, M; Van der Weken, D; De Witte, V; Schulte, S; Melange, T; Kerre, E.E, "Color Image Retrieval using Fuzzy Similarity Measures and Fuzzy Partitions", Image Processing, 2007. ICIP 2007. IEEE International Conference on,vol.6. pp.VI-13-VI-16.
  18. Liu Pengyu, Jia Kebin, Zhang Peizhen, "AN EFFECTIVE METHOD OF IMAHE RETRIEVAL BASED ON MODIFIED FUZZY C-MEANS CLUSTERING SCHEME", Signal Processing, 2006 8th International Conference on, vol.3. 
  19. Gao, J., et al., A wavelet transform-based image segmentation method. Optik, 2020. 208: p. 164123. 
  20. Fuentes-Pacheco, J., et al., Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network. Remote Sensing, 2019. 11(10): p. 1157. 
  21. Long, X. and J. Sun, Image segmentation based on the minimum spanning tree with a novel weight. Optik, 2020. 221: p. 165308. 
  22. Adhikari, S.K., et al., Conditional spatial fuzzy C-means clustering algorithm for segmentation of MRI images. Applied Soft Computing, 2015(0). 
  23. Yao, H., et al., An improved k-means clustering algorithm for fish image segmentation. Mathematical and Computer Modelling, 2013. 58(3-4): p. 790-798.  https://doi.org/10.1016/j.mcm.2012.12.025
  24. Abbasgholipour, M., et al., Color image segmentation with genetic algorithm in a raisin sorting system based on machine vision in variable conditions. Expert Systems with Applications, 2011. 38(4): p. 3671-3678.  https://doi.org/10.1016/j.eswa.2010.09.023
  25. Abbasgholipour, M., Omid, M., & Borghei, A. M. (2006). Development of an efficient algorithm for grading raisin based on color features. In Proceedings of the international conference on innovations in food and bioprocess technologies, December, AIT, Pathumthani, Thailand (pp. 12-14). 
  26. Zhang, Y., et al., Image segmentation using PSO and PCM with Mahalanobis distance. Expert Systems with Applications, 2011. 38(7): p. 9036-9040.  https://doi.org/10.1016/j.eswa.2011.01.041
  27. Tan, K.S., N.A. Mat Isa, and W.H. Lim, Color image segmentation using adaptive unsupervised clustering approach. Applied Soft Computing, 2013. 13(4): p. 2017-2036.  https://doi.org/10.1016/j.asoc.2012.11.038
  28. Benaichouche, A.N., H. Oulhadj, and P. Siarry, Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction. Digital Signal Processing, 2013. 23(5): p. 1390-1400.  https://doi.org/10.1016/j.dsp.2013.07.005
  29. Ayala, H.V.H., et al., Image thresholding segmentation based on a novel beta differential evolution approach. Expert Systems with Applications, 2015. 42(4): p. 2136-2142.  https://doi.org/10.1016/j.eswa.2014.09.043
  30. Wang, X.-y., et al., Color image segmentation using PDTDFB domain hidden Markov tree model. Applied Soft Computing, 2015. 29(0): p. 138-152.  https://doi.org/10.1016/j.asoc.2014.12.023
  31. Wang, Z., Peng, B., Huang, Y. and Sun, G., 2019. Classification for plastic bottles recycling based on image recognition. Waste management, 88, pp.170-181.  https://doi.org/10.1016/j.wasman.2019.03.032
  32. Wu, R., Zhang, B. and Zhao, D.E., 2020, March. Classification of common recyclable garbage based on hyperspectral imaging and deep learning. In 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology (Vol. 11438, p. 1143807). International Society for Optics and Photonics. 
  33. Ozkan, K., Ergin, S., Isik, S. and Isikli, I., 2015. A new classification scheme of plastic wastes based upon recycling labels. Waste Management, 35, pp.29-35. https://doi.org/10.1016/j.wasman.2014.09.030