Real Time Detection Of Waste Type Using Single Shot Multibox Detector

Abdiansah Abdiansah, M. Qurhanul Rizqie, M. Hatta Aldino Ramadhan

Abstract

The lack of human initiative to manage their own wastes is one of many reasons why waste management in residential area is not optimal. A system to detect waste type in real time is a necessity to support the waste management process to be faster and optimal. This research propose the waste type detection systems using 2 types of Single Shot Multibox Detector models, SSD300 and SSD512. Both models were compared based on the accuracy and speed of detection on TACO dataset dan Waste Classification Data. SSD512 achieves a better accuracy of 0.63 mAP compared to the accuracy of SSD300, which is 0.57 mAP. Both models can also be said to be real time, with the SSD300's detection speed being faster at 51 fps compared to the SSD512's detection speed at 28 fps.

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References

S. Bansal, S. Patel, I. Shah, A. Patel, J. Makwana, and R. Thakker, “AGDC: Automatic Garbage Detection and Collection”, arXiv preprint arXiv:1908.05849v1, 2019.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, and A.C. Berg, “SSD: Single Shot Multibox Detector”, arXiv preprint arXiv:1512.02325v5, 2016. (main references)

P.F. Proenca and P. Simões, “TACO: Trash Annotations in Context for Litter Detection”, arXiv preprint arXiv: 2003.06975v2, 2020.

J. Deng, W. Dong, R. Socher, L-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database”, IEEE conference on computer vision and pattern recognition 2009:248-255, 2009.

D. Erhan, C. Szegedy, A. Toshev and D. Anguelov, “Scalable Object Detection using Deep Neural Networks”, arXiv preprint arXiv:1312.2249v1, 2013.

K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition”, 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc, 2015.

L-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A.L. Yuille, “DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution,and Fully Connected CRFs”, arXiv preprint arXiv:1606.00915v2, 2017.

M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn and A Zisserman, "The PASCAL Visual Object Classes Challenge: A Retrospective", International Journal of Computer Vision 111(1):98-136, 2015.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long , R. Girshick, S. Guadarrama, and T. Darrell, Caffe: Convolutional Architecture for Fast Feature Embedding”, arXiv preprint arXiv:1408.5093v1, 2014.

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks”, In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS’10), 2010.

DOI: https://doi.org/10.36706/sjia.v3i1.34

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