International Journal of Advanced Culture Technology

The International Promotion Agency of Culture Technology (국제문화기술진흥원)
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Municipal waste classification system design based on Faster-RCNN and YoloV4 mixed model

  • Liu, Gan (Computer Engineering, Honam University) ;
  • Lee, Sang-Hyun (Department of Computer Engineering, Honam University)
  • Received : 2021.07.31
  • Accepted : 2021.08.30
  • Published : 2021.09.30
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Abstract

Currently, due to COVID-19, household waste has a lot of impact on the environment due to packaging of food delivery. In this paper, we design and implement Faster-RCNN, SSD, and YOLOv4 models for municipal waste detection and classification. The data set explores two types of plastics, which account for a large proportion of household waste, and the types of aluminum cans. To classify the plastic type and the aluminum can type, 1,083 aluminum can types and 1,003 plastic types were studied. In addition, in order to increase the accuracy, we compare and evaluate the loss value and the accuracy value for the detection of municipal waste classification using Faster-RCNN, SDD, and YoloV4 three models. As a final result of this paper, the average precision value of the SSD model is 99.99%, the average precision value of plastics is 97.65%, and the mAP value is 99.78%, which is the best result.

Keywords

References

  1. "Korea resource recirculation Information system", http://210.104.107.10/rrs/main.do
  2. Abhishek Gupta, Alagan Anpalagan, Ling Guan, Ahmed Shaharyar Khwaja, "Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues,"ELSEVIER, scienceDirect, Vol. 10, July 2021. DOI: https://doi.org/10.1016/j.array.2021.100057.
  3. X.L. Xie, Recognition and Positioning of Sorting Robot Based on Deep Learning, An-Hui Engineering University, CN, 2019.
  4. Y.F.Zhang. Research and Implementation of Person Detection System Based on Deep Learning, Beijing University of Posts and Telecommunications, CN, 2020.
  5. https://blog.csdn.net/Payforr/article/details/107392539.
  6. Simonyan, K. and Andrew Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition." CoRR, pp.1409-1556, 2015.
  7. H.Xu, D.G.Yang, Q.Q.Jiang, L.J.Lin, "Improvement of Lightweight Vehicle Detection Network Based on SSD" Computer Engineering and Applications, pp.1-10, June 2021.
  8. S. Ren, K. He, R. Girshick and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 1 June 2017. DOI: 10.1109/TPAMI.2016.2577031.
  9. "ZhiHu", ZhiHu, last modified, accessed Sep 06, 2021, https://zhuanlan.zhihu.com/p/31426458.
  10. "dobong-gu Seoul", Korea Cleaning Administration Website, last modified June 02, 2021, accessed Sep 06, 2021, https://www.dobong.go.kr/subsite/waste/Contents.asp?code=10007358
  11. S. Yun, D. Han, S. Chun, S. J. Oh, Y. Yoo and J. Choe, "CutMix: Regularization Strategy to Train Strong Classifiers With Localizable Features," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 6022-6031, 2019. DOI: 10.1109/ICCV.2019.00612.
  12. HE K, ZHANG X, REN S, et al. "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition" IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, No. 9, pp.1904-1916, 2015. DOI: 10.1109/TPAMI.2015.2389824
  13. "PyTorch", PyTorch, last modified, accessed June 02, 2021, https://pytorch.org/docs/master/generated/torch.nn.Upsample.html
  14. Uijlings, J. R. R, VanDesande, K. E. A, Smeulders, A. W. M. and Gevers, T. "Selective Search for Object Recognition," International Journal of Computer Vision, Vol. 104, No. 2, pp. 154-171, 2013. DOI: 10.1007/s11263-013-0620-5