Abstract
Littering quantification is an important step for improving cleanliness of cities. When human interpretation is too cumbersome or in some cases impossible, an objective index of cleanliness could reduce the littering by awareness actions. In this paper, we present a fully automated computer vision application for littering quantification based on images taken from the streets and sidewalks. We have employed a deep learning based framework to localize and classify different types of wastes. Since there was no waste dataset available, we built our acquisition system mounted on a vehicle. Collected images containing different types of wastes. These images are then annotated for training and benchmarking the developed system. Our results on real case scenarios show accurate detection of littering on variant backgrounds.
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References
Sermanet, P., Eigen, D., Zhang, X., et al.: Overfeat: integrated recognition, localization and detection using convolutional networks. In: ICLR (2014)
Mittal, G., Yagnik, K.B., Garg, M., Krishnan, N.: Spotgarbage: smartphone app to detect garbage using deep learning. In: UbiComp (2016)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)
Sudha, S., Vidhyalakshmi, M., Pavithra, K., et al.: An automatic classification method for environment. In: TIAR (2016)
Carlos, B.L.J., Alejandro, R., Manuel, E.: Automatic waste classification using computer vision as an application in colombian high schools. In: LACNEM (2015)
Sakr, G., Mokbel, M., Darwich, A.: Comparing deep learning and support vector machines for autonomous waste sorting. In: IMCIT (2016)
Stewart, R., Andriluka, M.: End-to-end people detection in crowded scenes. In: CVPR (2015)
Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. CoRR, abs/1409.4842 (2014)
Abadi, M., Agarwal, A., Barham, P., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
Deng, J., Dong, W., Socher, R., et al.: ImageNet: a large-scale hierarchical image database. In: CVPR (2009)
Jia, Y., Caffe, S., et al.: Convolutional architecture for fast feature embedding. arXiv:1408.5093’14
Everingham, M., Van Gool, L., Williams, C.K.I., et al.: The pascal visual object classes (VOC) challenge. In: IJCV (2010)
Acknowledgment
The authors would like to thank Mr. Niels Michel, Manager of Dialog & Service at City of Zurich for sharing his in-depth experience on cleanliness measurement thus significantly contributing to this project.
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Rad, M.S. et al. (2017). A Computer Vision System to Localize and Classify Wastes on the Streets. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_18
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DOI: https://doi.org/10.1007/978-3-319-68345-4_18
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