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A Novel Recyclable Garbage Detection System for Waste-to-energy Based On Optimized CenterNet With Feature Fusion

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Abstract

The detection of recyclable garbage plays an important role in waste-to-energy and carbon neutrality. However, due to the complexity of waste accumulation in waste-to-energy plants, the current garbage grabbing algorithms have limited accuracy. In order to overcome the above problems and avoid waste of resources, a novel recyclable garbage detection algorithm and corresponding system are studied in this paper. The CenterNet model is optimized by feature fusion so that it can better extract the subtle features of garbage. The YOLO model and original CenterNet model are adopted for garbage detection, and the backbone of YOLO model is optimized with VGG and DenseNet. Based on it, a garbage detection system is designed and a recyclable garbage dataset is constructed. The validation results show that the algorithm proposed in this paper is efficient and valid.

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References

  1. Zhang, L. C. (2018). Research on key techniques of apple sorting based on machine vision. Shihezi University.

  2. Lv, C., et al. (2020). Research on Intelligent Sorting System, Hardware and software design of MSW by Machine Vision. Intelligent City, 6(20), 2.

    Google Scholar 

  3. Han, F., Shan, Y., Cekander, R., Sawhney, H. S., & Kumar, R. (2006). A two-stage approach to people and vehicle detection with hog-based svm. In Performance Metrics for Intelligent Systems 2006 Workshop (pp. 133-140).

  4. Felzenszwalb, P., McAllester, D., & Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. In 2008 IEEE conference on computer vision and pattern recognition (pp. 1-8). Ieee.

  5. Dong, C., Loy, C. C., He, K., et al. (2014). Learning a Deep Convolutional Network for Image Super-Resolution. Springer International Publishing.

    Book  Google Scholar 

  6. Girshick, R., Donahue, J., Darrell, T., et al. (2017). Rich feature hierarchies for accurate object detection and semantic segmentation. Technical Report (v5).

  7. Tian, Z., Shen, C., Chen, H., et al. (2020). FCOS: Fully convolutional one-stage object detection. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE.

  8. Du, L., Zhang, R., & Wang, X. (2020). Overview of Two-stage object detection algorithms. Journal of Physics: Conference Series, 1544(1):012033 (6pp).

  9. Uijlings, J. R. R., Van De Sande, K. E. A., Gevers, T., et al. (2013). Selective search for object recognition. International journal of computer vision, 104(2), 154–171.

    Article  Google Scholar 

  10. He, K., Zhang, X., Ren, S., et al. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9), 1904–1916.

    Article  Google Scholar 

  11. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems28.

  12. Ren, S., He, K., Girshick, R., et al. (2017). Faster R-CNN: Towards real-time object detection with region proposal networks.

  13. Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).

  14. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).

  15. Leibe, B., Matas, J., Sebe, N., et al. (2016). Lecture Notes in Computer Science, SSD: Single Shot MultiBox Detector. Computer Vision ECCV, 9905, 2016.

    Google Scholar 

  16. Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, Faster, Stronger. IEEE, 6517–6525.

  17. Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental improvement. arXiv e-prints.

  18. Bochkovskiy, A., Wang, C. Y., & Liao H. (2020). YOLOv4: Optimal Speed and accuracy of object detection. 

  19. Law, H., & Deng, J. (2020). CornerNet: Detecting Objects as Paired Keypoints. International Journal of Computer Vision, 128(3), 642–656.

    Article  Google Scholar 

  20. Duan, K., Bai, S., Xie, L., et al. (2019).  Centernet: Keypoint triplets for object detection. Proceedings of the IEEE/CVF international conference on computer vision, 6569–6578.

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Funding

The project is financially supported by the Social Development Project of Jiangsu Key R&D Program (BE2022680), the Ministry of Industry and Information Technology of China (No. 2021-R-43), the National Natural Science Foundation of China (No.61972214) and Jiangsu Postdoctoral Science Foundation(1601039B).

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Correspondence to Xiaogang Cheng or Limin Song.

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The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the result.

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Cheng, X., Hu, F., Song, L. et al. A Novel Recyclable Garbage Detection System for Waste-to-energy Based On Optimized CenterNet With Feature Fusion. J Sign Process Syst 95, 67–76 (2023). https://doi.org/10.1007/s11265-022-01811-1

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