A Computer Vision System to Localize and Classify Wastes on the Streets

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.

[1]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  B. L. Juan Carlos,et al.  Automatic Waste Classification using Computer Vision as an Application in Colombian High Schools , 2015 .

[3]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[4]  S. Sudha,et al.  An automatic classification method for environment: Friendly waste segregation using deep learning , 2016, 2016 IEEE Technological Innovations in ICT for Agriculture and Rural Development (TIAR).

[5]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[6]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[7]  Li Fei-Fei,et al.  ImageNet: A large-scale hierarchical image database , 2009, CVPR.

[8]  George E. Sakr,et al.  Comparing deep learning and support vector machines for autonomous waste sorting , 2016, 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET).

[9]  Luc Van Gool,et al.  The Pascal Visual Object Classes (VOC) Challenge , 2010, International Journal of Computer Vision.

[10]  Andrew Y. Ng,et al.  End-to-End People Detection in Crowded Scenes , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Gaurav Mittal,et al.  SpotGarbage: smartphone app to detect garbage using deep learning , 2016, UbiComp.