Volume 3 • Issue 1 • PP: 13-26 • 2024
On The Analysis of Some Deep Learning Algorithms for Object Detection and Applications
View PDF Abstract
The traditional methods of discovering objects no longer meet the requirements of the times as a result of their reliance on non-dynamic methods and as a result of their slow performance in light of the world's dependence on a huge amount of multimedia and social media. With the rapid development of deep learning providing more powerful tools capable of manipulating high-level and complex semantic features of objects. Several techniques have been developed to detect objects using deep learning algorithms. This research presents a comparative analysis of the most famous deep learning techniques for object detection, explaining their mechanisms, use cases and an experimental evaluation of their performance.
Keywords
References
[1]. Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
[2]. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
[3]. Wu, X., Sahoo, D., & Hoi, S. C. (2020). Recent advances in deep learning for object detection. Neurocomputing, 396, 39-64.
[4]. Jiao, L., Zhang, R., Liu, F., Yang, S., Hou, B., Li, L., & Tang, X. (2021). New generation deep learning for video object detection: A survey. IEEE Transactions on Neural Networks and Learning Systems.
[5]. Pitts, W., & McCulloch, W. S. (1947). How we know universals the perception of auditory and visual forms. The Bulletin of mathematical biophysics, 9(3), 127-147.
[6]. Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. science, 313(5786), 504-507.
[7]. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June).Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). Ieee.
[8]. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
[9]. Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229.
[10]. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
[11]. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409 . 1556.
[12]. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer
vision and pattern recognition (pp. 770-778).
[13]. O'Shea, K., & Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.
[14]. Chan, T. H., Jia, K., Gao, S., Lu, J., Zeng, Z., & Ma, Y. (2015). PCANet: A simple deep learning baseline for image classification?. IEEE transactions on image processing, 24(12), 5017-5032.
[15]. Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). Ieee.
[16]. Dalal, N., Triggs, B., & Schmid, C. (2006, May). Human detection using oriented histograms of flow and appearance. In European conference on computer vision (pp. 428-441). Springer, Berlin, Heidelberg.
[17]. Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
[18]. Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards realtime object detection with region proposal networks. Advances in neural information processing systems, 28.
[19]. He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 2961-2969).
[20]. Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117- 2125).
[21]. Pramanik, A., Pal, S. K., Maiti, J., & Mitra, P. (2021). Granulated RCNN and multi-class deep sort for multi-object detection and tracking. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(1), 171-181.
[22]. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016, October). Ssd: Single shot multibox detector. In European conference on computer vision (pp. 21-37). Springer, Cham.
[23]. Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988).
[24]. Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
[25]. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
[26]. Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision, 104(2), 154-171.
[27]. Zou, Z., Shi, Z., Guo, Y., & Ye, J. (2019). Object detection in 20 years: A survey. arXiv preprint arXiv:1905.05055.
[28]. Aziz, L., Salam, M. S. B. H., Sheikh, U. U., & Ayub, S. (2020). Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review. IEEE Access, 8, 170461-170495.
Cite This Article
Choose your preferred format