ABSTRACT
Maintaining a clean and hygienic civic environment is an indispensable yet formidable task, especially in developing countries. With the aim of engaging citizens to track and report on their neighborhoods, this paper presents a novel smartphone app, called SpotGarbage, which detects and coarsely segments garbage regions in a user-clicked geo-tagged image. The app utilizes the proposed deep architecture of fully convolutional networks for detecting garbage in images. The model has been trained on a newly introduced Garbage In Images (GINI) dataset, achieving a mean accuracy of 87.69%. The paper also proposes optimizations in the network architecture resulting in a reduction of 87.9% in memory usage and 96.8% in prediction time with no loss in accuracy, facilitating its usage in resource constrained smartphones.
Supplemental Material
Available for Download
Supplemental material.
- Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari, "What is an object?", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2010.Google ScholarCross Ref
- Bogdan Alexe, Thomas Deselaers, and Vittorio Ferrari. "Measuring the objectness of image windows", IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11), 2189--2202, 2012. Google ScholarDigital Library
- Sean Bell, Paul Upchurch, Noah Snavely, and Kavita Bala, "Material recognition in the wild with the materials in context database." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3479--3487, 2015.Google Scholar
- Yoshua Bengio, "Learning deep architectures for AI", Journal of Foundations and trends® in Machine Learning, 2(1), 1--127, 2009. Google ScholarDigital Library
- Wenlin Chen, James T. Wilson, Stephen Tyree, Kilian Q. Weinberger, and Yixin Chen, "Compressing neural networks with the hashing trick", arXiv preprint arXiv:1504.04788, 2015.Google Scholar
- Navneet Dalal, and Bill Triggs, "Histograms of oriented gradients for human detection", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 886--893, 2005. Google ScholarDigital Library
- Emily L Denton, WOjciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fegus, "Exploiting linear structure within convolutional networks for efficient evaluation", Advances in Neural Information Processing Systems, 2014. Google ScholarDigital Library
- Clement Farabet, C Couprie, L Najman, and Yann LeCun, "Learning hierarchical features for scene labeling", IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1915--1929, 2013. Google ScholarDigital Library
- P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, "Object detection with discriminatively trained part-based models", IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9), 1627--1645, 2010. Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, "Deep Residual Learning for Image Recognition", arXiv preprint arXiv:1512.03385, 2013.Google Scholar
- Anil K. Jain, and Farshid Farrokhnia, "Unsupervised texture segmentation using Gabor filters", Proceedings of the IEEE Conference on Systems, Man and Cybernetics, 14--19, 1990.Google Scholar
- Anil K. Jain, Nalini K. Ratha, and Sridhar Lakshmanan, "Object detection using Gabor filters,", Pattern Recognition 30(2), 295--309, 1997.Google ScholarCross Ref
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, "Imagenet classification with deep convolutional neural networks", Advances in Neural Information Processing Systems, 2012. Google ScholarDigital Library
- Gil Levi, and Tal Hassner, "Age and gender classification using convolutional neural networks", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 34--42, 2015.Google ScholarCross Ref
- Jonathan Long, Evan Shelhamer, and Trevor Darrell. "Fully convolutional networks for semantic segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.Google ScholarCross Ref
- David G. Lowe, "Distinctive image features from scale-invariant keypoints", International Journal of Computer Vision, 60(2) 91--110, 2004. Google ScholarDigital Library
- Bangalore S. Manjunath, and Wei-Ying Ma, "Texture features for browsing and retrieval of image data", IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8), 837--842, 1997. Google ScholarDigital Library
- Wanli Ouyang, et al., "Deepid-net: multi-stage and deformable deep convolutional neural networks for object detection", Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2015.Google Scholar
- Florent Perronnin, Jorge SÃąnchez, and Thomas Mensink, "Improving the fisher kernel for large-scale image classification", Proceedings of the European Conference on Computer Vision, 143--156, 2010. Google ScholarDigital Library
- Pedro H.O. Pinheiro, and Ronan Collober, "Recurrent convolutional neural networks for scene parsing", arXiv preprint arXiv:1306.2795, 2013.Google Scholar
- Mahek Shah, "Swachh Bharat - Clean India Android App", at https://goo.gl/BG5KOJGoogle Scholar
- Karen Simonyan, and Andrew Zisserman, "Very deep convolutional networks for large-scale image recognition", Proceedings of the International Conference on Learning Representations, 2015.Google Scholar
- Christian Szegedy, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.Google Scholar
- Jasper R.R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders, "Selective search for object recognition", International Journal of computer vision, 104(2), 154--171, 2013. Google ScholarDigital Library
- "caffe-android-lib," at https://github.com/sh1r0/caffe-android-libGoogle Scholar
- "Swachchh Delhi App fails to meet expectations," at http://goo.gl/VqYDKPGoogle Scholar
- "Frugal innovation and innovation for the frugal" at http://goo.gl/c7MX6kGoogle Scholar
Index Terms
- SpotGarbage: smartphone app to detect garbage using deep learning
Recommendations
Developing mobile apps using cross-platform frameworks: a case study
HCI'13: Proceedings of the 15th international conference on Human-Computer Interaction: human-centred design approaches, methods, tools, and environments - Volume Part IIn last few years, a huge variety of frameworks for the mobile cross-platform development have been released to deliver quick and overall better solutions. Most of them are based on different approaches and technologies; therefore, relying on only one ...
PScout: analyzing the Android permission specification
CCS '12: Proceedings of the 2012 ACM conference on Computer and communications securityModern smartphone operating systems (OSs) have been developed with a greater emphasis on security and protecting privacy. One of the mechanisms these systems use to protect users is a permission system, which requires developers to declare what ...
AI Benchmark: Running Deep Neural Networks on Android Smartphones
Computer Vision – ECCV 2018 WorkshopsAbstractOver the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, ...
Comments