Abstract
Computer vision is an artificial intelligence (AI) subfield that enables computers and systems to extract information from digital photos, movies, and other visual inputs. Detection systems are an important use in industrial production lines. In this paper, an automatic small abnormal object system is designed. First, the author obtains an image devoid of anomalous objects, which is then processed using the Candy filter to produce the standard form. Second, define the primary pattern, sub pattern 1, sub pattern 2, and the deviation between the new image and the original image. Then, we use template matching and background subtraction to identify questionable locations. Finally, live picture features will be compared to original image features. With Candy filter, the precision will be enhanced. The findings of image processing will be transmitted to operate the automatic abnormal removal equipment. The result indicates an accuracy of ~ 90%. The processing time is < 5 s, which has no effect on the production line cycle time.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
R.S. Peres, X. Jia, J. Lee, K. Sun, A.W. Colombo, J. Barata, Industrial artificial intelligence in industry 4.0: systematic review, challenges and outlook. IEEE Access 8, 220121–220139 (2020)
T.-V. Dang, N.-T. Bui, Multi-scale fully convolutional network-based semantic segmentation for mobile robot navigation. Electronics 12(3), 1–18 (2023)
T.-V. Dang, N.-T. Bui, Research and design obstacle avoidance strategy for indoor autonomous mobile robot using monocular camera. J. Adv. Transp. (2022)
T.-V. Dang, Smart home management system with face recognition based on arc face model in deep convolutional neural network. J. Robot. Control 3(6), 754–761 (2022)
T.-V. Dang, D.-S. Nguyen, Optimal navigation based on improved A* algorithm for mobile robot, in International Conference on Intelligent Systems and Networks 2023, 18–19 March, Hanoi, Vietnam (2023)
T.-V. Dang, Smart attendance system based on improved facial recognition. J. Robot. Control 4(1), 46–54 (2023)
I. Delibasoglu, Moving object detection method with motion regions tracking in background subtraction. Signal, Image and Video Processing (2023)
S.K. Choudhury, P.K. Sa, S. Bakshi, B. Majhi, Combining background subtraction and convolutional neural network for anomaly detection in pum**-unit surveillance. IEEE Access 4, 6133–6150 (2016)
I.K.A. Murinto, Detection of object changes in low contrast image using template matching method, in Proceedings of the 2nd International Conference on Engineering and Applied Sciences (2nd InCEAS), IOP Conferences Series: Materials Science and Engineering, p. 771 (2020)
S. Sharma, Shape-texture debiased training for robust template matching. Sensors 22, 6658 (2022)
Acknowledgements
This research is funded by Hanoi University of Science and Technology (HUST) under project number T2022-PC-029. The authors express grateful thankfulness to Vietnam-Japan International Institute for Science of Technology (VJIIST), School of Mechanical Engineering, HUST, Vietnam and Shibaura Institute of Technology, Japan.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Viet, D.T., Bui, NT. (2024). Design the Abnormal Object Detection System Using Template Matching and Subtract Background Algorithm. In: Long, B.T., et al. Proceedings of the 3rd Annual International Conference on Material, Machines and Methods for Sustainable Development (MMMS2022). MMMS 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-57460-3_10
Download citation
DOI: https://doi.org/10.1007/978-3-031-57460-3_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-57459-7
Online ISBN: 978-3-031-57460-3
eBook Packages: EngineeringEngineering (R0)