Accident Scene Image Identification Technique using Convolutional Neural Network
|
10.5120/ijca2021921706 |
Omogbhemhe M.I. and Odegua R.O.. Accident Scene Image Identification Technique using Convolutional Neural Network. International Journal of Computer Applications 183(32):5-7, October 2021. BibTeX
@article{10.5120/ijca2021921706,
author = {Omogbhemhe M.I. and Odegua R.O.},
title = {Accident Scene Image Identification Technique using Convolutional Neural Network},
journal = {International Journal of Computer Applications},
issue_date = {October 2021},
volume = {183},
number = {32},
month = {Oct},
year = {2021},
issn = {0975-8887},
pages = {5-7},
numpages = {3},
url = {http://www.ijcaonline.org/archives/volume183/number32/32135-2021921706},
doi = {10.5120/ijca2021921706},
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
Abstract
Building intelligent software that can effectively detect accident scene with the help of Google map has suffered set back because of the poor ability of the currently used software to effectively detect, identify and classify accident scene images from non accident scene images. Hence there is need for a better technique of implementing this software. In this paper, Convolutional neural networks (CNN) which is a part of deep learning algorithm was used to provide a better classification technique that any software to be developed for the purpose of detecting accident scene image can adopt. The algorithm was tested on 4000 accident scene images with other kind of images (cats and dogs) by adopting models of other researchers. In this paper, classification accuracy and Mean Squared Error (MSE) were used to evaluate the algorithm in identifying accident scene images accurately. The result was further presented using a graph of MSE against a number of trained epochs. The result of the experiment shows accuracy in the image classification and identification.
References
- Lillesand, T.M. and Kiefer, R.W. and Chipman, J.W.(2004). in “Remote Sensing and Image Interpretation” 5th ed. Wiley, 2004
- Li Deng and Dong Yu (2014). “Deep Learning: methods and applications” by Microsoft research [Online] available at: http://research.microsoft.com/pubs/209355/NOW-Book Revised- Feb2014-online.pdf
- McCulloch, Warren; Walter Pitts"A Logical Calculus of Ideas Im- manent in Nervous Activity”, Bulletin of Mathematical Biophysics 5 (4): 115–133(1943)
- An introduction to convolutional neural networks [Online]availableat:http://white.stanford.edu/teach/index.php/An_Introduction_to _Convolutional_Neural_Networks
- Hubel, D. and Wiesel, T. (1968). Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology (Lon- don), 195, 215–243C. J. Kaufman, Rocky Mountain Research Labora- tories, Boulder, Colo., personal communication, 1992. (Personal communication)
- Yann LeCun, Leon Bottou, Yodhua Bengio and Patrick Haffner, “Gradient -Based Learning Applied to Document Recognition”, Proc. Of IEEE, November 1998.
- S. L. Phung and A. Bouzerdoum,”MATLAB library for convolutional neural network,” Technical Report, ICT Research Institute, Visual and Audio Signal Processing Laboratory, University of Wollongong. Available at: http://www.uow.edu.au/˜phung
- Deepika J, Sowmya.V and Soman K.P (2015). Image Classification Using Convolutional Neural Networks. International Journal of Advancements in Research & Technology, Volume 3, Issue 6
- Adelson, Edward H., Charles H. Anderson, James R. Bergen, Peter J. Burt, and Joan M. Ogden. "Pyramid methods in image processing." RCA engineer 29, no. 6 (1984): 33-41.
Keywords
Image Identification, Accident Scene, Convolutional Neural Network, Classification, Algorithm