Object Detector for Waste Detection by Modifying Feature Pyramid Networks to Enhance Feature Fusion
| dc.contributor.author | Monjur, Ocean | |
| dc.contributor.author | Shams, Mohammad Galib | |
| dc.contributor.author | Mahmud, Faysal | |
| dc.date.accessioned | 2024-08-30T10:17:23Z | |
| dc.date.available | 2024-08-30T10:17:23Z | |
| dc.date.issued | 2023-05-30 | |
| dc.description | Supervised by Dr. Md. Hasanul Kabir, Co-supervisors, Mr. Md. Bakhtiar Hasan, Assistant Professor, Mr. Ahnaf Munir, Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh Assistant Professor | en_US |
| dc.identifier.citation | [1] Z. Zou, Z. Shi, Y. Guo, and J. Ye, “Object detection in 20 years: A survey,” 2019. [Online]. Available: https://arxiv.org/abs/1905.05055 [2] Y. Amit, P. Felzenszwalb, and R. Girshick, “Object detection,” Computer Vision: A Reference Guide, pp. 1–9, 2020. [3] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, pp. 436–444, 2015. [4] Z. Tan, J. Wang, X. Sun, M. Lin, H. Li et al., “Giraffedet: A heavy-neck paradigm for object detection,” in International Conference on Learning Representations, 2021. [5] S. Majchrowska, A. Mikołajczyk, M. Ferlin, Z. Klawikowska, M. A. Plantykow, A. Kwasigroch, and K. Majek, “Deep learning-based waste detection in natural and urban environments,” Waste Management, vol. 138, pp. 274–284, 2022. [6] A. B. Stambouli and E. Traversa, “Fuel cells, an alternative to standard sources of energy,” Renewable and sustainable energy reviews, vol. 6, no. 3, pp. 295–304, 2002. [7] W.-L. Mao, W.-C. Chen, H. I. K. Fathurrahman, and Y.-H. Lin, “Deep learning networks for real-time regional domestic waste detection,” Journal of Cleaner Production, vol. 344, p. 131096, 2022. [8] A. M. King, S. C. Burgess, W. Ijomah, and C. A. McMahon, “Reducing waste: repair, recondition, remanufacture or recycle?” Sustainable development, vol. 14, no. 4, pp. 257–267, 2006. [9] B. Ma, X. Li, Z. Jiang, and J. Jiang, “Recycle more, waste more? when recycling efforts increase resource consumption,” Journal of Cleaner Production, vol. 206, pp. 870–877, 2019. REFERENCES 30 [10] A. B. Wahyutama and M. Hwang, “Yolo-based object detection for separate collection of recyclables and capacity monitoring of trash bins,” Electronics, vol. 11, no. 9, p. 1323, 2022. [11] A. M. F. Durrani, A. U. Rehman, A. Farooq, J. A. Meo, and M. T. Sadiq, “An automated waste control management system (awcms) by using arduino,” in 2019 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, 2019, pp. 1–6. [12] D. Bashkirova, M. Abdelfattah, Z. Zhu, J. Akl, F. Alladkani, P. Hu, V. Ablavsky, B. Calli, S. A. Bargal, and K. Saenko, “Zerowaste dataset: Towards deformable object segmentation in cluttered scenes,” in Proceedings of the IEEE/CVF Con ference on Computer Vision and Pattern Recognition, 2022, pp. 21 147–21 157. [13] Y. Cheng, J. Zhu, M. Jiang, J. Fu, C. Pang, P. Wang, K. Sankaran, O. Onabola, Y. Liu, D. Liu et al., “Flow: A dataset and benchmark for floating waste detection in inland waters,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10 953–10 962. [14] P. F. Proenc¸a and P. Simoes, “Taco: Trash annotations in context for litter ˜ detection,” arXiv preprint arXiv:2003.06975, 2020. [15] M. Kraft, M. Piechocki, B. Ptak, and K. Walas, “Autonomous, onboard vision based trash and litter detection in low altitude aerial images collected by an unmanned aerial vehicle,” Remote Sensing, vol. 13, no. 5, p. 965, 2021. [16] M. S. Fulton, J. Hong, and J. Sattar, “Trash-icra19: A bounding box labeled dataset of underwater trash,” 2020. [17] M. Fulton, J. Hong, M. J. Islam, and J. Sattar, “Robotic detection of marine litter using deep visual detection models,” in 2019 international conference on robotics and automation (ICRA). IEEE, 2019, pp. 5752–5758. [18] T.-Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature ´ pyramid networks for object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 2117–2125. [19] S. Liu, L. Qi, H. Qin, J. Shi, and J. Jia, “Path aggregation network for instance segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 8759–8768. REFERENCES 31 [20] M. Tan, R. Pang, and Q. V. Le, “Efficientdet: Scalable and efficient object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 10 781–10 790. [21] S.-W. Kim, H.-K. Kook, J.-Y. Sun, M.-C. Kang, and S.-J. Ko, “Parallel fea ture pyramid network for object detection,” in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 234–250. [22] S. Seferbekov, V. Iglovikov, A. Buslaev, and A. Shvets, “Feature pyramid network for multi-class land segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018, pp. 272–275. [23] C. Deng, M. Wang, L. Liu, Y. Liu, and Y. Jiang, “Extended feature pyramid network for small object detection,” IEEE Transactions on Multimedia, vol. 24, pp. 1968–1979, 2021. [24] S. Qiao, L.-C. Chen, and A. Yuille, “Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 10 213–10 224. [25] C. Picron, T. Tuytelaars, and K. ESAT-PSI, “Trident pyramid networks for object detection,” 2022. [26] G. Zhao, W. Ge, and Y. Yu, “Graphfpn: Graph feature pyramid network for object detection,” in Proceedings of the IEEE/CVF international conference on computer vision, 2021, pp. 2763–2772. [27] J. Pang, K. Chen, J. Shi, H. Feng, W. Ouyang, and D. Lin, “Libra r-cnn: Towards balanced learning for object detection,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 821–830. [28] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778. [29] K. Chen, J. Wang, J. Pang, Y. Cao, Y. Xiong, X. Li, S. Sun, W. Feng, Z. Liu, J. Xu et al., “Mmdetection: Open mmlab detection toolbox and benchmark,” arXiv preprint arXiv:1906.07155, 2019. [30] D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980, 2014 | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/2146 | |
| dc.language.iso | en | en_US |
| dc.title | Object Detector for Waste Detection by Modifying Feature Pyramid Networks to Enhance Feature Fusion | en_US |
| dc.type | Thesis | en_US |
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