Transfer Learning for Abnormal Object Detection

Dung Nguyen

doi:10.54644/jte.2024.1526

Authors

Dung Nguyen University of Sciences, Hue University, Hue City, Vietnam https://orcid.org/0009-0000-4510-7504

Corressponding author's email:

nguyendung@hueuni.edu.vn

DOI:

https://doi.org/10.54644/jte.2024.1526

Keywords:

Machine learning, Transfer learning, Finetuning, Feature Extraction, Object Detection

Abstract

In today's world, smart surveillance plays an important role in protecting security and creating a safe living environment. For abnormal objects in the smart surveillance system, this is an important issue, requiring attention and timely response from managers and supervisors. To address this issue, the paper uses transfer learning techniques on modern object detection models to detect abnormal objects such as guns, knives, etc. in public places. We experimented with the transfer learning method on the DETR model with a small dataset, and the model results showed a fairly fast convergence speed. Through this method, we hope to help reduce the burden of public security monitoring and warning work for managers, while technicians can use transfer learning techniques that are deployed in practice.

Downloads: 0

Download data is not yet available.

Author Biography

Dung Nguyen, University of Sciences, Hue University, Hue City, Vietnam

Nguyen Dung was born on June 13, 1988 in Thua Thien Hue. He graduated with a bachelor’s degree in information technology from the College of Sciences, Hue University in 2010. In 2013, he graduated with a master’s degree in computer science from the College of Sciences, Hue University. Currently he works at the University of Sciences, Hue University.

Research fields: Software technology, artificial intelligence, machine learning, deep learning, databases

Email: nguyendung@hueuni.edu.vn. ORCID: https://orcid.org/0009-0000-4510-7504

References

F. Zhuang et al., "A comprehensive survey on transfer learning," Proceedings of the IEEE, vol. 109, no. 1, pp. 43-76, 2020. DOI: https://doi.org/10.1109/JPROC.2020.3004555

K. Weiss, T. M. Khoshgoftaar, and D. Wang, "A survey of transfer learning," Journal of Big data, vol. 3, no. 1, pp. 1-40, 2016. DOI: https://doi.org/10.1186/s40537-016-0043-6

A. Pasini, "Artificial neural networks for small dataset analysis," Journal of thoracic disease, vol. 7, no. 5, p. 953, 2015.

M. Bansal, M. Kumar, M. Sachdeva, and A. Mittal, "Transfer learning for image classification using VGG19: Caltech-101 image data set," Journal of ambient intelligence and humanized computing, pp. 1-12, 2021. DOI: https://doi.org/10.1007/s12652-021-03488-z

M. Shaha and M. Pawar, "Transfer learning for image classification," in 2018 second international conference on electronics, communication and aerospace technology (ICECA), 2018: IEEE, pp. 656-660. DOI: https://doi.org/10.1109/ICECA.2018.8474802

H. E. Kim, A. Cosa-Linan, N. Santhanam, M. Jannesari, M. E. Maros, and T. Ganslandt, "Transfer learning for medical image classification: a literature review," BMC medical imaging, vol. 22, no. 1, p. 69, 2022. DOI: https://doi.org/10.1186/s12880-022-00793-7

N. Agarwal, A. Sondhi, K. Chopra, and G. Singh, "Transfer learning: Survey and classification," Smart Innovations in Communication and Computational Sciences: Proceedings of ICSICCS 2020, pp. 145-155, 2021. DOI: https://doi.org/10.1007/978-981-15-5345-5_13

L. Zhao, S. Pan, E. Xiang, E. Zhong, Z. Lu, and Q. Yang, "Active transfer learning for cross-system recommendation," in Proceedings of the AAAI Conference on Artificial Intelligence, 2013, vol. 27, no. 1, pp. 1205-1211. DOI: https://doi.org/10.1609/aaai.v27i1.8458

Z. Lin, D. Liu, W. Pan, and Z. Ming, "Transfer learning in collaborative recommendation for bias reduction," in Proceedings of the 15th ACM Conference on Recommender Systems, 2021, pp. 736-740. DOI: https://doi.org/10.1145/3460231.3478860

R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 580-587. DOI: https://doi.org/10.1109/CVPR.2014.81

J. R. Uijlings, K. E. Van De Sande, T. Gevers, and A. W. Smeulders, "Selective search for object recognition," International journal of computer vision, vol. 104, pp. 154-171, 2013. DOI: https://doi.org/10.1007/s11263-013-0620-5

A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," Advances in neural information processing systems, vol. 25, 2012.

R. Girshick, "Fast r-cnn," in Proceedings of the IEEE international conference on computer vision, 2015, pp. 1440-1448. DOI: https://doi.org/10.1109/ICCV.2015.169

K. He, X. Zhang, S. Ren, and J. Sun, "Spatial pyramid pooling in deep convolutional networks for visual recognition," IEEE transactions on pattern analysis and machine intelligence, vol. 37, no. 9, pp. 1904-1916, 2015. DOI: https://doi.org/10.1109/TPAMI.2015.2389824

S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," Advances in neural information processing systems, vol. 28, 2015.

A. Vaswani et al., "Attention is all you need," Advances in neural information processing systems, vol. 30, 2017.

N. Carion, F. Massa, G. Synnaeve, N. Usunier, A. Kirillov, and S. Zagoruyko, "End-to-end object detection with transformers," in European conference on computer vision, 2020: Springer, pp. 213-229. DOI: https://doi.org/10.1007/978-3-030-58452-8_13

K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778. DOI: https://doi.org/10.1109/CVPR.2016.90

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779-788. DOI: https://doi.org/10.1109/CVPR.2016.91

J. Redmon and A. Farhadi, "YOLO9000: better, faster, stronger," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263-7271. DOI: https://doi.org/10.1109/CVPR.2017.690

J. Redmon and A. Farhadi, "Yolov3: An incremental improvement," arXiv preprint arXiv:1804.02767, 2018.

C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 7464-7475. DOI: https://doi.org/10.1109/CVPR52729.2023.00721

A. Hosna, E. Merry, J. Gyalmo, Z. Alom, Z. Aung, and M. A. Azim, "Transfer learning: a friendly introduction," Journal of Big Data, vol. 9, no. 1, p. 102, 2022. DOI: https://doi.org/10.1186/s40537-022-00652-w

H. D. Nguyen and C. Sakama, "Feature learning by least generalization," in International Conference on Inductive Logic Programming, 2021: Springer, pp. 193-202. DOI: https://doi.org/10.1007/978-3-030-97454-1_14

Gun Detectiongun Dataset, Roboflow [Online]. Available: https://universe.roboflow.com/ruclan99999-mail-ru/gun-detectiongun