Abstract
The environment protection is becoming, now more than ever, a serious consideration of all government, non-government, and industrial organizations. The problem of littering and garbage is severe, particularly in developing countries. The problem of littering is that it has a compounding effect, and unless the litter is reported and cleaned right away, it tends to compound and become an even more significant problem. To raise awareness of this problem and to allow a future automated solution, we propose developing a garbage detecting system for detection and segmentation of garbage in images. For this reason, we use deep semantic segmentation approach to train a garbage segmentation model. Due to the small dataset for the task, we use transfer learning of pre-trained model that is adjusted to this specific problem. For this particular experiment, we also develop our own dataset to build segmentation models. In general, the deep semantic segmentation approaches combined with transfer learning, give promising results. They show great potential towards developing a garbage detection application that can be used by the public services and by concerned citizens to report garbage pollution problems in their communities.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). http://tensorflow.org/. Software available from tensorflow.org
Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481–2495 (2017)
Briones, A.G., et al.: Use of gamification techniques to encourage garbage recycling. A smart city approach. In: Uden, L., Hadzima, B., Ting, I.-H. (eds.) KMO 2018. CCIS, vol. 877, pp. 674–685. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-95204-8_56
Brown, D.P.: Garbage: how population, landmass, and development interact with culture in the production of waste. Resour. Conserv. Recycl. 98, 41–54 (2015). http://www.sciencedirect.com/science/article/pii/S0921344915000440
Carvana: Carvana image masking challenge automatically identify the boundaries of the car in an image. https://www.kaggle.com/c/carvana-image-masking-challenge/. Accessed 30 May 2019
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833–851. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_49
Chollet, F., et al.: Keras (2015). https://github.com/fchollet/keras. Accessed 30 May 2019
Corizzo, R., Ceci, M., Japkowicz, N.: Anomaly detection and repair for accurate predictions in geo-distributed big data. Big Data Res. 16, 18–35 (2019)
Corizzo, R., Ceci, M., Zdravevski, E., Japkowicz, N.: Scalable auto-encoders for gravitational waves detection from time series data. Expert. Syst. Appl. 151, 113378 (2020)
Iglovikov, V., Shvets, A.: TernausNet: U-Net with VGG11 encoder pre-trained on ImageNet for image segmentation. ArXiv e-prints (2018)
Kumar, N.S., Vuayalakshmi, B., Prarthana, R.J., Shankar, A.: IoT based smart garbage alert system using Arduino UNO. In: 2016 IEEE Region 10 Conference (TENCON), pp. 1028–1034, November 2016
Lameski, J., Jovanov, A., Zdravevski, E., Lameski, P.L., Gievska, S.: Skin lesion segmentation with deep learning. In: IEEE EUROCON 2019–18th International Conference on Smart Technologies. IEEE (2019). https://doi.org/10.1109/EUROCON.2019.8861636
Mittal, G., Yagnik, K.B., Garg, M., Krishnan, N.C.: SpotgarBage: smartphone app to detect garbage using deep learning. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, pp. 940–945. ACM, New York (2016)
Petrovska, B., Atanasova-Pacemska, T., Corizzo, R., Mignone, P., Lameski, P., Zdravevski, E.: Aerial scene classification through fine-tuning with adaptive learning rates and label smoothing. Appl. Sci. 10, 5792 (2020)
Petrovska, B., Zdravevski, E., Lameski, P., Corizzo, R., Stajduhar, I., Lerga, J.: Deep learning for feature extraction in remote sensing: a case-study of aerial scene classification. Sensors 15(1), 1 (2020)
Prajakta, G., Kalyani, J., Snehal, M.: Smart garbage collection system in residential area. IJRET Int. J. Res. Eng. Technol. 4(03), 122–124 (2015)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Ryan, S., Corizzo, R., Kiringa, I., Japkowicz, N.: Pattern and anomaly localization in complex and dynamic data. In: 2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1756–1763 (2019)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Acknowledgments
The work presented in this paper was partially funded by the Ss. Cyril and Methodius University in Skopje, Faculty of Computer Science and Engineering. We also gratefully acknowledge the support of NVIDIA Corporation through a grant providing GPU resources for this work. We also acknowledge the support of the Microsoft AI for Earth for providing processing resources.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Despotovski, A., Despotovski, F., Lameski, J., Zdravevski, E., Kulakov, A., Lameski, P. (2020). Towards Cleaner Environments by Automated Garbage Detection in Images. In: Dimitrova, V., Dimitrovski, I. (eds) ICT Innovations 2020. Machine Learning and Applications. ICT Innovations 2020. Communications in Computer and Information Science, vol 1316. Springer, Cham. https://doi.org/10.1007/978-3-030-62098-1_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-62098-1_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62097-4
Online ISBN: 978-3-030-62098-1
eBook Packages: Computer ScienceComputer Science (R0)