Yolo Deep Learning Model Based Algorithm for Object Detection
DOI:
https://doi.org/10.26438/ijcse/v8i1.174178Keywords:
Digital image processing, image recognition, Accuracy, time complexity, histogram, K-MEANS, YOLOAbstract
With the growth of deep learning and digital image processing, it is require knowing about the facts of deep learning. Deep learning is major factor of object detection. In existing research R-CNN algorithm used for detect the objects from an image while in this proposed research method we are using YOLO (you only look once) algorithm to detect the different objects in a single image. This is less time consuming because we only recognize a image once and we detect the whole objects in an image.
References
[1] Yali Amit and Pedro Felzenszwalb, University of Chicago
[2] Ross, Girshick (2014). "Rich feature hierarchies for accurate object detection and semantic segmentation" (PDF). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE. doi:10.1109/CVPR.2014.81
[3] "ETHZ - Computer Vision Lab: Publications". Vision.ee.ethz.ch. Retrieved 2013-10-09.
[4] Jussi Jokela “PERSON COUNTER USING REAL-TIME OBJECT DETECTION AND A SMALL NEURAL NETWORK” TURKU UNIVERSITY OF APPLIED SCIENCES
[5] http://cs231n.github.io/convolutional-networks/
[6] ImageNet Classification with Deep Convolutional Neural Networks by Alex Krizhevsky University of Toronto kriz@cs.utoronto.ca Ilya Sutskever University of Toronto ilya@cs.utoronto.ca
[7] Wenqing Chu, Deng Cai “ Deep feature based contextual model for object detection” Neurocomputing 275, 1035–1042, 2018
[8] S. Gidaris , N. Komodakis , Locnet: improving localization accuracy for object de- tection, in: Proceedings of the IEEE Conference on Computer Vision and Pat- tern Recognition, pp. 789–798, 2016.
[9] Gemma Roig_1;2 Xavier Boix_1;2 Horesh Ben Shitrit2 Pascal Fua2 “Conditional Random Fields for Multi-Camera Object Detection”1ETHZ, Zurich (Switzerland) 2EPFL, Lausanne (Switzerland)
[10] Navneet Dalal, Bill Triggs. Histograms of Oriented Gradients for Human Detection. Cordelia Schmid and Stefano Soatto and Carlo Tomasi. International Conference on Computer Vision.
[11] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS, 2015.
[12] Wei Liu1, Dragomir Anguelov2, Dumitru Erhan “SSD: Single Shot MultiBox Detector” UNC Chapel Hill 2Zoox Inc. 3Google Inc. 4University of Michigan, Ann-Arbor 1wliu@cs.unc.edu, 2drago@zoox.com.
[13] Joseph Redmon University of Washington pjreddie@cs.washington.edu Santosh Divvala Allen Institute for Artificial Intelligence” You Only Look Once: Unified, Real-Time Object Detection”
[14] Albert Soto i Serrano” YOLO Object Detector for Onboard Driving Images” , ESCOLA D’ENGINYERIA (EE), UNIVERSITAT AUTONOMA DE BARCELONA (UAB)
[15] Z. Shen and X. Xue. Do more dropouts in pool5 feature maps for better object detection. arXiv preprint arXiv:1409.6911, 2014.
[16] R. B. Girshick. Fast R-CNN. CoRR, abs/1504.08083, 2015.
[17] Geethapriya. S, N. Duraimurugan, S.P. Chokkalingam, International Journal of Engineering and Advanced Technology (IJEAT) ISSN: 2249 – 8958, Volume-8, Issue-3S, February 2019.
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