Abstract
Object detection and classification is a very important integrant of computer vision domain. It has its role in various sectors of life as security, safety, fun, heath & comfort etc. Under safety and security, surveillance is one critical application area where, Object detection has gained the growing importance. Object in such case could be human being and other suspicious and sensitive objects. Correct detection and classification on accuracy measures is always a challenge in these problems. Now days, deep learning techniques are getting utilized as an effective and efficient tool for different classification problems. Looking over these facts, a review of available deep learning architectures has been presented in this paper, for the problem of object detection and classification. The classification models considered for review are AlexNet, VGG Net, GoogLeNet, ResNet. The dataset used for experimentation is Caltech-101 dataset and the standard performance measures utilized for evaluation are True Positive Rate (TPR), False Positive Rate (FPR) and Accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Agarwal, A., Gupta, S., Singh, D.K.: Review of optical flow technique for moving object detection. In: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), pp. 409–413. IEEE, December 2016
Shin, H.C., et al.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)
Xia, X., Xu, C., Nan, B.: Inception-v3 for flower classification. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), pp. 783–787. IEEE, June 2017
Singh, D.K.: Gaussian elliptical fitting based skin color modeling for human detection. In: 2017 IEEE 8th Control and System Graduate Research Colloquium (ICSGRC), pp. 197–201. IEEE, August 2017
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Ansari, M.A., Singh, D.K. (2019). Review of Deep Learning Techniques for Object Detection and Classification. In: Verma, S., Tomar, R., Chaurasia, B., Singh, V., Abawajy, J. (eds) Communication, Networks and Computing. CNC 2018. Communications in Computer and Information Science, vol 839. Springer, Singapore. https://doi.org/10.1007/978-981-13-2372-0_37
Download citation
DOI: https://doi.org/10.1007/978-981-13-2372-0_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2371-3
Online ISBN: 978-981-13-2372-0
eBook Packages: Computer ScienceComputer Science (R0)