Cited By
View all- Zhu ZLi XZhai JHu H(2024)PODB: A learning-based polarimetric object detection benchmark for road scenes in adverse weather conditionsInformation Fusion10.1016/j.inffus.2024.102385108(102385)Online publication date: Aug-2024
Identification of Recycled Plastic Bottles by Convolutional Neural Network Based Polarization Information: Leveraging Polarization Information for Accurate Sorting and Recycling of Plastic Bottles
Authors: 
Zhiying Tan, Tao Gong, Zilong Wang, Gang LiAuthors Info & Claims
Zhiying Tan, Tao Gong, Zilong Wang, Gang LiAuthors Info & ClaimsArticle No.: 3, Pages 1 - 10
Abstract
The identification and classification of polyethylene terephthalate (PET) and non-PET plastic bottles is of great significance in recycling. Compared with RGB images, polarized images contain more information about the surface materials of objects. A recognition and classification method based on polarimetric vision combined with convolution neural network and image segmentation is proposed. Firstly, different polarization information is used to train a convolutional network, filter the polarization angle information as the most suitable training feature, and create an angle of polarization dataset of plastic bottles. Afterwards, the convolutional neural network is structurally optimized to achieve high accuracy in classifying plastic bottles. Further, to solve the problem of overlapping or contacting plastic bottles in practical inspection, this paper proposes an image segmentation method based on dynamic threshold segmentation and convex package defect detection to achieve high recognition accuracy. The optimized convolutional neural network combined with the image segmentation method can achieve a classification accuracy of 96.10%.
References
[1]
M. Sulyman, J. Haponiuk, K. Formela.: Utilization of Recycled Polyethylene Terephthalate (PET) in Engineering Materials: A Review. International Journal of Environmental Science & Development. vol. 7, no.2, pp. 100-108, Jan. 2016.
[2]
Davari, M.R., Ostad Movahed, S.: The Flotation by Selected Depressants as an Efficient Technique for Separation of a Mixed Acrylonitrile Butadiene Styrene, Polycarbonate and Polyoxymethyleneplastics in Waste Streams. J Polym Environ 27, 1709–1720 (2019). https://doi.org/10.1007/s10924-019-01467-2.
[3]
Sangobtip Pongstabodee, Napatr Kunachitpimol, Somsak Damronglerd.: Combination of three-stage sink–float method and selective flotation technique for separation of mixed post-consumer plastic waste. Waste Management. pp. 475-483 (2007). https://doi.org/10.1016/j.wasman.2007.03.005.
[4]
Park CH, Jeon HS, Yu HS, Han OH, Park JK.: Application of electrostatic separation to the recycling of plastic wastes: separation of PVC, PET, and ABS. Environmental Science & Technology. 2008 Jan;42(1):249-255. 10.1021/es070698h. 18350904.
[5]
M.T. Carvalho, E. Agante, F. Durão.: Recovery of PET from packaging plastics mixtures by wet shaking table. Waste Management. pp. 1747-1754 (2006). https://doi.org/10.1016/j.wasman.2006.08.015.
[6]
Sattmann R, Mönch I, Krause H, : Laser-Induced Breakdown Spectroscopy for Polymer Identification. Applied Spectroscopy. 1998;52(3):456-461. 10.1366/0003702981943680.
[7]
Anzano, J.M., Gornushkin, I.B., Smith, B.W. and Winefordner, J.D. (2000).: Laser-induced plasma spectroscopy for plastic identification. Polym Eng Sci, 40: 2423-2429. https://doi.org/10.1002/pen.11374.
[8]
Mohammed A. Gondal & Mohammad N. Siddiqui (2007) Identification of different kinds of plastics using laser-induced breakdown spectroscopy for waste management, Journal of Environmental Science and Health, Part A, 42:13, 1989-1997. 10.1080/10934520701628973.
[9]
Amine Kassouf, Jacqueline Maalouly, Douglas N. Rutledge, Hanna Chebib, Violette Ducruet.: Rapid discrimination of plastic packaging materials using MIR spectroscopy coupled with independent components analysis (ICA). Waste Management. pp. 2131-2138 (2014). https://doi.org/10.1016/j.wasman.2014.06.015.
[10]
Moroni, M. : PET and PVC Separation with Hyperspectral Imagery. Sensors. 15, 1, 2205–2227 (2015). https://doi.org/10.3390/s150102205.
[11]
A. Ulrici, S. Serranti, C. Ferrari, D. Cesare, G. Foca, G. Bonifazi.: Efficient chemometric strategies for PET–PLA discrimination in recycling plants using hyperspectral imaging. Chemometrics and Intelligent Laboratory Systems. pp. 31-39 (2013). https://doi.org/10.1016/j.chemolab.2013.01.001.
[12]
Sementsov, D.I., Grechishkin, R.M.: Polarization characteristics and magneto-optic diffraction efficiency at oblique light incidence. Czech J Phys 39, 1413–1422 (1989). https://doi.org/10.1007/BF01597606.
[13]
A. Mahendru and M. Sarkar.: Bio-inspired object classification using polarization imaging. Sixth International Conference on Sensing Technology (ICST). pp. 207-212(2012). 10.1109/ICSensT.2012.6461672.
[14]
M. Sarkar, D. S. S. San Segundo Bello, C. van Hoof and A. Theuwissen.: Integrated Polarization Analyzing CMOS Image Sensor for Material Classification. IEEE Sensors Journal, vol. 11, no. 8, pp. 1692-1703, Aug. 2011. 10.1109/JSEN.2010.2095003.
[15]
Fukushima, K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybernetics 36, 193–202 (1980). https://doi.org/10.1007/BF00344251.
[16]
A. A. M. Al-Saffar, H. Tao and M. A. Talab.: Review of deep convolution neural network in image classification. International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), pp. 26-31 (2017). 10.1109/ICRAMET.2017.8253139.
[17]
Kaul, A, Raina, S.: Support vector machine versus convolutional neural network for hyperspectral image classification: A systematic review. Concurrency Computat Pract Exper. e6945 (2022). 10.1002/cpe.6945.
[18]
Wartini Ng, Budiman Minasny, Maryam Montazerolghaem, Jose Padarian, Richard Ferguson, Scarlett Bailey, Alex B. McBratney.: Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma, pp. 251-267 (2019). https://doi.org/10.1016/j.geoderma.2019.06.016.
[19]
J. Redmon, S. Divvala, R. Girshick and A. Farhadi.: You Only Look Once: Unified, Real-Time Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779-788 (2016). 10.1109/CVPR.2016.91.
[20]
Yunong Tian, Guodong Yang, Zhe Wang, Hao Wang, En Li, Zize Liang.: Apple detection during different growth stages in orchards using the improved YOLO-V3 model. Computers and Electronics in Agriculture. pp. 417-426 (2019). https://doi.org/10.1016/j.compag.2019.01.012.
[21]
Dewi, C., Chen, RC. & Yu, H.: Weight analysis for various prohibitory sign detection and recognition using deep learning. Multimed Tools Appl 79, 32897–32915 (2020). https://doi.org/10.1007/s11042-020-09509-x.
[22]
Mohammed A. Al-masni, Mugahed A. Al-antari, Jeong-Min Park, Geon Gi, Tae-Yeon Kim, Patricio Rivera, Edwin Valarezo, Mun-Taek Choi, Seung-Moo Han, Tae-Seong Kim.: Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Computer Methods and Programs in Biomedicine. pp. 85–94 (2018). 10.1016/j.cmpb.2018.01.017.
[23]
Z. -Q. Zhao, P. Zheng, S. -T. Xu and X. Wu.: Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212-3232, Nov. 2019 10.1109/TNNLS.2018.2876865.
[24]
Ng W, Minasny B, Montazerolghaem M, Padarian J, Ferguson R, Bailey S, McBratney AB (2019).: Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra. Geoderma 352:251–267. https://doi.org/10.1016/j.geoderma.2019.06.016.
[25]
Z. -Q. Zhao, P. Zheng, S. -T. Xu and X. Wu.: Object Detection With Deep Learning: A Review. IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 11, pp. 3212-3232, Nov. 2019. 10.1109/TNNLS.2018.2876865.
[26]
Ryo Hasegawa, Yutaro Iwamoto, and Yen-Wei Chen, "Robust Japanese Road Sign Detection and Recognition in Complex Scenes Using Convolutional Neural Networks," Journal of Image and Graphics, Vol. 8, No. 3, pp. 59-66, September 2020.
[27]
J. Redmon, S. Divvala, R. B. Girshick, and A. Farhadi.: You Only Look Once: Unified, Real-Time Object Detection. Proc. IEEE Conference on Computer Vision and Pattern Recognition. pp. 779-788(2016).
[28]
J. Kuruvilla, D. Sukumaran, A. Sankar and S. P. Joy.: A review on image processing and image segmentation. International Conference on Data Mining and Advanced Computing (SAPIENCE), pp. 198-203 (2016), 10.1109/SAPIENCE.2016.7684170.
[29]
Song, S., Liao, M. & Qin, J. Multiresolution image dynamic thresholding. Machine Vis. Apps. 3, 13–16 (1990). https://doi.org/10.1007/BF01211448.
[30]
Zhaokun Wang, Binbin Peng, Yanjun Huang, Guanqun Sun.: Classification for plastic bottles recycling based on image recognition. Waste Managemen. pp. 170-181 (2019). https://doi.org/10.1016/j.wasman.2019.03.032.
Index Terms
- Identification of Recycled Plastic Bottles by Convolutional Neural Network Based Polarization Information: Leveraging Polarization Information for Accurate Sorting and Recycling of Plastic Bottles
Recommendations
Simultaneous Detection and Segmentation of Cell Nuclei based on Convolutional Neural Network
ISICDM 2018: Proceedings of the 2nd International Symposium on Image Computing and Digital MedicineIn this paper, we mainly focus on automatic detection and segmentation of cell nuclei in histopathology images. Though some methods have been presented to solve these issues, there is scope for efficiency and performance to improve. We propose an end-to-...
Cerebral aneurysm image segmentation based on multi-modal convolutional neural network
Highlights- Cerebral aneurysm and adjacent vessels can be segmented by convolutional neural network efficiently.
AbstractBackground and Objective: Accurate segmentation of cerebral aneurysms in computed tomography angiography (CTA) can provide an essential reference for diagnosis and treatment. This study aimed to evaluate a more helpful image ...
3D anisotropie convolutional neural network with step transfer learning for liver segmentation
ICCIP '18: Proceedings of the 4th International Conference on Communication and Information ProcessingAutomatic liver segmentation on computed tomography (CT) slices plays an important role in current clinical practice for liver cancer supporting diagnosis. While manual segmentation is accurate and precise, it is time-consuming and tedious. Otherwise, ...
Comments
Information & Contributors
Information
Published In
May 2023
711 pages
ISBN:9798400708237
DOI:10.1145/3604078
Copyright © 2023 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 26 October 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICDIP 2023
ICDIP 2023: The 15th International Conference on Digital Image Processing
May 19 - 22, 2023
Nanjing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 40Total Downloads
- Downloads (Last 12 months)34
- Downloads (Last 6 weeks)1
Reflects downloads up to 04 Jan 2025
Other Metrics
Citations
Cited By
View all- Zhu ZLi XZhai JHu H(2024)PODB: A learning-based polarimetric object detection benchmark for road scenes in adverse weather conditionsInformation Fusion10.1016/j.inffus.2024.102385108(102385)Online publication date: Aug-2024
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format