Analysis of object recognition trends based on deep learning

Research Article
Open access

Analysis of object recognition trends based on deep learning

Siwei Cao 1*
  • 1 Department of Electronic and Information Engineering, Anhui Architecture University, Hefei, Anhui Province, 230601, China.    
  • *corresponding author csw0321@std.ahjzu.edu.cn
ACE Vol.5
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-57-7
ISBN (Online): 978-1-915371-58-4

Abstract

With the increasing development and maturity of deep learning, computers have also made world-renowned achievements in the domain of vision, especially in the basic and core branch of object detection, giving birth to many classical algorithms, which are widely used in many fields such as autonomous driving, intelligent medical care, intelligent security, and search entertainment. Before the emergence of deep learning algorithms, traditional algorithms for object detection were usually divided into three stages: region selection, feature extraction, and feature classification. However, with the advent of deep learning algorithms, object detection has taken to another peak, with Single Shot MultiBox Detector (SSD) enabling first-order detection of multi-feature maps and Region-based Convolutional Neural Networks (R-CNN) improving the performance of object detection while enabling instance segmentation. For object detection, this paper investigates the traditional algorithms, R-CNN, SSD, You Only Look Once (YOLO), and diffusion model, which is influential detection algorithms, and compares their differences as well as advantages in object detection to provide a reference for related research.

Keywords:

Convolutional Neural Networks, Deep Learning, Object Recognition.

Cao,S. (2023). Analysis of object recognition trends based on deep learning. Applied and Computational Engineering,5,292-299.
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References

[1]. Glover, G. H., and Pauly, J. M. (1992). Projection reconstruction techniques for reduction of motion effects in MRI. Magnetic resonance in medicine, 28(2), 275-289.

[2]. Sturm, P. (2005, June). Multi-view geometry for general camera models. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (Vol. 1, pp. 206-212). IEEE.

[3]. Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11), 1330-1334.

[4]. Sage, K., and Young, S. (1999). Security applications of computer vision. IEEE aerospace and electronic systems magazine, 14(4), 19-29.

[5]. Beiker, S. A. (2012). Legal aspects of autonomous driving. Santa Clara L. Rev., 52, 1145.

[6]. Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., ... and Yang, R. (2018). The apolloscape dataset for autonomous driving. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 954-960).

[7]. Geiger, A., Lenz, P., Stiller, C., and Urtasun, R. (2013). Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11), 1231-1237.

[8]. Ho, J., Jain, A., and Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840-6851.

[9]. Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L. (2022). Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11461-11471).

[10]. Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., and Murthy, K. R. K. (2001). Improvements to Platt's SMO algorithm for SVM classifier design. Neural computation, 13(3), 637-649.

[11]. Bansal, M., Kumar, M., Kumar, M., and Kumar, K. (2021). An efficient technique for object recognition using Shi-Tomasi corner detection algorithm. Soft Computing, 25(6), 4423-4432.

[12]. Yu, X., Meng, X., Jiang, X., Zhu, Z., and Li, X. (2022, April). Research on defect recognition method of substation inspection images based on faster R-CNN. In International Conference on Internet of Things and Machine Learning (IoTML 2021) (Vol. 12174, pp. 329-337). SPIE.

[13]. Meng, R., Rice, S. G., Wang, J., and Sun, X. (2018). A fusion steganographic algorithm based on faster R-CNN. Computers, Materials and Continua, 55(1), 1-16.

[14]. Beini, Z., Xuee, C., Bo, L., and Weijia, W. (2021). A new few-shot learning method of digital PCR image detection. IEEE Access, 9, 74446-74453.

[15]. Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J. (2022). Geodiff: A geometric diffusion model for molecular conformation generation. arXiv preprint arXiv:2203.02923.

[16]. Wijmans, J. G., and Baker, R. W. (1995). The solution-diffusion model: a review. journal of membrane science, 107(1-2), 1-21.

[17]. Ho, J., Jain, A., and Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840-6851.

Cite this article

Cao,S. (2023). Analysis of object recognition trends based on deep learning. Applied and Computational Engineering,5,292-299.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 3rd International Conference on Signal Processing and Machine Learning

ISBN：978-1-915371-57-7(Print) / 978-1-915371-58-4(Online)
Editor：Omer Burak Istanbullu
Conference website: http://www.confspml.org
Conference date: 25 February 2023
Series: Applied and Computational Engineering
Volume number: Vol.5
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Glover, G. H., and Pauly, J. M. (1992). Projection reconstruction techniques for reduction of motion effects in MRI. Magnetic resonance in medicine, 28(2), 275-289.

[2]. Sturm, P. (2005, June). Multi-view geometry for general camera models. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (Vol. 1, pp. 206-212). IEEE.

[3]. Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence, 22(11), 1330-1334.

[4]. Sage, K., and Young, S. (1999). Security applications of computer vision. IEEE aerospace and electronic systems magazine, 14(4), 19-29.

[5]. Beiker, S. A. (2012). Legal aspects of autonomous driving. Santa Clara L. Rev., 52, 1145.

[6]. Huang, X., Cheng, X., Geng, Q., Cao, B., Zhou, D., Wang, P., ... and Yang, R. (2018). The apolloscape dataset for autonomous driving. In Proceedings of the IEEE conference on computer vision and pattern recognition workshops (pp. 954-960).

[7]. Geiger, A., Lenz, P., Stiller, C., and Urtasun, R. (2013). Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11), 1231-1237.

[8]. Ho, J., Jain, A., and Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840-6851.

[9]. Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., and Van Gool, L. (2022). Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 11461-11471).

[10]. Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., and Murthy, K. R. K. (2001). Improvements to Platt's SMO algorithm for SVM classifier design. Neural computation, 13(3), 637-649.

[11]. Bansal, M., Kumar, M., Kumar, M., and Kumar, K. (2021). An efficient technique for object recognition using Shi-Tomasi corner detection algorithm. Soft Computing, 25(6), 4423-4432.

[12]. Yu, X., Meng, X., Jiang, X., Zhu, Z., and Li, X. (2022, April). Research on defect recognition method of substation inspection images based on faster R-CNN. In International Conference on Internet of Things and Machine Learning (IoTML 2021) (Vol. 12174, pp. 329-337). SPIE.

[13]. Meng, R., Rice, S. G., Wang, J., and Sun, X. (2018). A fusion steganographic algorithm based on faster R-CNN. Computers, Materials and Continua, 55(1), 1-16.

[14]. Beini, Z., Xuee, C., Bo, L., and Weijia, W. (2021). A new few-shot learning method of digital PCR image detection. IEEE Access, 9, 74446-74453.

[15]. Xu, M., Yu, L., Song, Y., Shi, C., Ermon, S., and Tang, J. (2022). Geodiff: A geometric diffusion model for molecular conformation generation. arXiv preprint arXiv:2203.02923.

[16]. Wijmans, J. G., and Baker, R. W. (1995). The solution-diffusion model: a review. journal of membrane science, 107(1-2), 1-21.

[17]. Ho, J., Jain, A., and Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33, 6840-6851.

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