International Journal of Microelectronics and Digital integrated circuits
Region-based Object Detection and Classification using Faster R-CNN
Abstract
With the advent of Deep Learning the machine learning systems are able to recognize and classify objects of interest in an image. Various advancement has been done in the field of object recognition and classification. Our research work focusses on improving the R-CNN, Fast R-CNN, YOLO architecture. The work focused on using Region Proposals Network (RPN) to extract region of interest in an image. RPN outputs an image based on the objectness score. The output objects are subjected to Roll Polling for classification. Our research work focusses on training Faster R-CNN using custom based data set of images. Our trained network efficiently detects objects from an image consisting of multiple objects. Our network requires minimum GPU capability of 3. 0 or higher.Keywords: Convolution neural network, deep learning, faster R-CNN, region proposal networkReferences (94)
- Ren, Shaoqing, “Faster R-CNN: Towards Real-Time Object
- detection with Region Proposal Networks.” Advances in Neural
- Information Processing Systems. 2015.
- Girshick, Ross. “Fast r-cnn.”Proceedings of the IEEE
- International Conference on Computer Vision. 2015.
- Uijlings, Jasper RR, “Selective search for object
- recognition.” International Journal of Computer Vision
- (2013): 154-171.
- R. Girshick, “Fast R-CNN,” in IEEE International
- Conference on Computer Vision (ICCV), 2015.
- J. R. Uijlings, K. E. van de Sande, T. Gevers, and A. W.
- Smeulders,“Selective search for object recognition,” International
- Journal of Computer Vision (IJCV), 2013.
- C. L. Zitnick and P. Dollar, “Edge boxes: Locating object
- proposals from edges,” in European Conference on Computer
- Vision (ECCV), 2014.
- P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D.
- Ramanan, “Object detection with discriminatively trained
- partbased models,” IEEE Transactions on Pattern Analysis and
- Machine Intelligence (TPAMI), 2010.
- S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN:
- Towards real-time object detection with region proposal
- networks,” in Neural Information Processing Systems (NIPS),
- J. Zhu, X. Chen, and A. L. Yuille, “DeePM: A deep part-
- based model for object detection and semantic part localization,”
- arXiv:1511.07131, 2015.
- J. Johnson, A. Karpathy, and L. Fei-Fei, “Densecap: Fully
- convolutional localization networks for dense captioning,”
- arXiv:1511.07571, 2015.
- J. Hosang, R. Benenson, and B. Schiele, “How good are
- detection proposals, really?” in British Machine Vision
- Conference (BMVC), 2014.
- N. Chavali, H. Agrawal, A. Mahendru, and D. Batra,
- “Object-Proposal Evaluation Protocol is ’Gameable’,” arXiv:
- S. Ren, K. He, R. Girshick, X. Zhang, and J. Sun, “Object
- detection networks on convolutional feature maps,”
- arXiv:1504.06066, 2015.
- C. Szegedy, A. Toshev, and D. Erhan, “Deep neural
- networks for object detection,” in Neural Information Processing
- Systems (NIPS), 2013.
- C. Szegedy, S. Reed, D. Erhan, and D. Anguelov,
- “Scalable, high-quality object detection,” arXiv:1412.1441 (v1),
- J. Dai, K. He, and J. Sun, “Convolutional feature masking
- for joint object and stuff segmentation,” in IEEE Conference on
- Computer Vision and Pattern Recognition (CVPR), 2015.
- J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and
- Y. Bengio,“Attention-based models for speech recognition,”in
- Neural Information Processing Systems (NIPS), 2015.
- V. Nair and G. E. Hinton, “Rectified linear units improve
- restricted boltzmann machines,” in International Conference on
- Machine Learning (ICML), 2010.
- Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E.
- Howard, W. Hubbard, and L. D. Jackel, “Backpropagation
- applied to handwritten zip code recognition,” Neural
- computation, 1989.
- A. Krizhevsky, I. Sutskever, and G. Hinton, “Imagenet
- classification with deep convolutional neural networks,” inNeural
- Information Processing Systems (NIPS), 2012.
- Girshick, Ross, "Rich feature hierarchies for accurate
- object detection and semantic segmentation." Proceedings of the
- IEEE Conference on Computer Vision and Pattern Recognition.
- Zitnick, C. Lawrence, and Piotr Dollar. "Edge boxes:
- Locating object proposals from edges." European Conference on
- Computer Vision 2014. Springer International Publishing, 2014.
- K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid
- pooling in deep convolutional networks for visual recognition,”
- in European Conference on Computer Vision (ECCV), 2014
- K. Simonyan and A. Zisserman, “Very deep convolutional
- networks for large-scale image recognition,” in International
- Conference on Learning Representations (ICLR), 2015.
- R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich
- feature hierarchies for accurate object detection and semantic
- segmentation,” in IEEE Conference on Computer Vision and
- Pattern Recognition (CVPR), 2014.
- J. Long, E. Shelhamer, and T. Darrell, “Fully
- convolutional networks for semantic segmentation,” in IEEE
- Conference on Computer Vision and Pattern Recognition
- P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus,
- and Y. LeCun, “Overfeat: Integrated recognition, localization and
- detection using convolutional networks,” in International
- Conference on Learning Representations (ICLR), 2014.
- C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D.
- Anguelov, D. Erhan, and A. Rabinovich, “Going deeper with
- convolutions,” in IEEE Conference on Computer Vision and
- Pattern Recognition (CVPR), 2015.
- Li Z, Zhang L, Fang Y, Wang J, Xu H, Yin B, Lu H, “Deep
- People Counting with Faster R-CNN and Correlation Tracking,”
- Proceedings of the International Conference on Internet
- Multimedia Computing and Service - ICIMCS'16 (2016) pp. 57-
- Object Detection using fasterR-CNN,2017[Online]
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