[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

A deep learning and transfer learning model for intra-change detection in images

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

On-shelf availability (OSA) refers to the number of products available in saleable condition to a customer at the place he expects and at the time he wants to buy the product. OSA of the products in retail stores is important to enhance the customer shopping experience and profitability. The products on the retail store shelves are arranged strategically. This arrangement may be changed during shopping, because it is observed that customers may not move the products in the same or at the right place when they pick them up from the shelf. In such real-time scenarios, it would be appropriate to perform shelf image analysis to detect and identify these misplaced objects. This will help the shopkeeper to maintain a large-scale store effortlessly, otherwise it will require massive human effort and labour along with time. Many computer vision-based technology solutions have been developed by researchers to solve this problem. This article discusses a convolutional neural network (CNN)-based method for classifying shelf imagery into the correct semantic classes (that is whether it has misplaced products or not). The proposed architecture was evaluated with a modified COIL-100 dataset. The results of the proposed CNN model were compared with the variant of the proposed model in addition to lightweight pre-trained deep learning models viz; mobilenetV2, densenet121, efficientnetb0, efficientnetb3, and NASNetMobile using transfer learning (TL) concept. According to this study, it has been found that the TL-based MobilenetV2 model is lightweight and achieves better classification performance with 91.28% accuracy. Furthermore, a CNN-based model with 11 user-defined layers provides an accuracy of 90.36%. This shows the efficacy of the proposed models for change detection.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The dataset that support the findings of this study are available on request from the corresponding author, Bagyammal T, e-mail:t_bagyammal@cb.amrita.edu.

Code availability

Source code may be available on request from the corresponding author, Bagyammal T, e-mail: t_bagyammal@cb.amrita.edu.

References

  1. Agnihotram G, Vepakomma N, Trivedi S, Laha S, Isaacs N, Khatravath S, Naik P, Kumar R (2017) Combination of advanced robotics and computer vision for shelf analytics in a retail store. In: 2017 International Conference on Information Technology (ICIT). pp 119–124

  2. Chun P, Yamane T, Tsuzuki Y (2021) Automatic detection of cracks in asphalt pavement using deep learning to overcome weaknesses in images and GIS visualization. Appl Sci 11:892

    Article  Google Scholar 

  3. Pazzaglia G, Mameli M, Frontoni E, Zingaretti P, Pietrini R, Manco D, Placidi V (2021) A deep learning approach for product detection in intelligent retail environment. In: International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol 85437. pp V007T07A010

  4. Moorthy R, Behera S, Verma S (2015) On-shelf availability in retailing. Int J Comput Appl 115:47–51

    Google Scholar 

  5. Moorthy R, Behera S, Verma S, Bhargave S, Ramanathan P (2015) Applying image processing for detecting on-shelf availability and product positioning in retail stores. In: Proceedings of the Third International Symposium on Women in Computing and Informatics. pp 451–457

  6. MM, TB, Parameswaran L, Vaiapury K (2018) An Automated Vision Based Change Detection Method for Planogram Compliance in Retail Stores. Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_33

  7. Bagyammal T, Latha P, Karthikeyan V (2022) Intra change detection in shelf images using fast discrete curvelet transform and features from accelerated segment test. In: High Performance Computing and Networking. pp 235–245

  8. Kaya A, Keceli A, Catal C, Yalic H, Temucin H, Tekinerdogan B (2019) Analysis of TL for deep neural network based plant classification models. Comput Electron Agric 158:20–29

    Article  Google Scholar 

  9. Zhang L, Yang F, Zhang Y, Zhu Y (2016) Road crack detection using deep convolutional neural network. In: 2016 IEEE International Conference on Image Processing (ICIP). pp 3708–3712

  10. Zhou T, Li J, Wang S, Tao R, Shen J (2020) Matnet: motion-attentive transition network for zero-shot video object segmentation. IEEE Trans Image Process 29:8326–8338

    Article  Google Scholar 

  11. Zhou T, Li L, Li X, Feng C, Li J, Shao L (2021) Group-wise learning for weakly supervised semantic segmentation. IEEE Trans Image Process 31:799–811

    Article  Google Scholar 

  12. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 4510–4520

  13. Howard A, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. ArXiv Preprint ArXiv:1704.04861

  14. Xiang Q, Wang X, Li R, Zhang G, Lai J, Hu Q (2019) Fruit image classification based on Mobilenetv2 with TL technique. In: Proceedings of the 3rd International Conference on Computer Science and Application Engineering. pp 1–7

  15. Howard A, Zhmoginov A, Chen L, Sandler M, Zhu M (2018) Inverted residuals and linear bottlenecks: mobile networks for classification, detection and segmentation

  16. Zoph B, Vasudevan V, Shlens J, Le Q (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 8697–8710

  17. Radhika K, Devika K, Aswathi T, Sreevidya P, Sowmya V, Soman K (2020) Performance analysis of NASNet on unconstrained ear recognition. In: Rout M, Rout J, Das H (eds) Nature inspired computing for data science. Springer, Cham, pp 57–82

    Chapter  Google Scholar 

  18. Saxen F, Werner P, Handrich S, Othman E, Dinges L, Al-Hamadi A (2019) Face attribute detection with mobilenetv2 and nasnet-Mobile. In: 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA). pp 176–180

  19. Huang G, Liu Z, Van Der Maaten L, Weinberger K (2017) Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp 4700–4708

  20. Rochmawanti O, Utaminingrum F (2021) Chest X-ray image to classify lung diseases in different resolution size using DenseNet-121 architectures. In: 6th International Conference on Sustainable Information Engineering and Technology 2021. pp 327–331

  21. Shaik S, Kirthiga S (2021) Automatic modulation classification using DenseNet. In: 2021 5th International Conference on Computer, Communication and Signal Processing (ICCCSP). pp 301–305

  22. Tan M, Le Q (2019) Efficientnet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning. pp 6105–6114

  23. LeCun Y (2015) LeNet-5, convolutional neural networks. URL: Http://yann. Lecun. Com/exdb/lenet 20:14

  24. Nielsen M (2015) Neural networks and deep learning. Determination press, San Francisco

    Google Scholar 

  25. Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning. ArXiv Preprint ArXiv:1811.03378

  26. Hao W, Yizhou W, Yaqin L, Zhili S (2020) The role of activation function in CNN. In: 2020 2nd International Conference on Information Technology and Computer Application (ITCA). pp 429–432

  27. Misra D (2019) Mish: a self regularized non-monotonic activation function. ArXiv Preprint ArXiv:1908.08681

  28. Kingma D, Ba J (2014) Adam: a method for stochastic optimization. ArXiv Preprint ArXiv:1412.6980

  29. Nene S, Nayar S, Murase H (1996) Others Columbia object image library (coil-100). Citeseer

  30. https://www.kaggle.com/datasets/geadalfa/cracked-non-cracked-surface-datasets, accessed on 15th August 2021

  31. Mishra C, Bagyammal T, Parameswaran L (2021) An algorithm design for anomaly detection in thermal images. In: Innovations in Electrical and Electronic Engineering. pp 633–650

  32. Dai W, Dai Y, Hirota K, Jia Z (2020) A flower classification approach with mobileNetV2 and transfer learning. In: Proceedings of the 9th International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2020), Beijing, China, vol 31

Download references

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Bagyammal Thirumurthy & Latha Parameswaran

  2. TCS Research and Innovation, IITM Research Park, Chennai, India

    Karthikeyan Vaiapury

Authors
  1. Bagyammal Thirumurthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Latha Parameswaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karthikeyan Vaiapury
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

BT, LP, and KV have contributed equally in ideas, algorithms, and result analysis. BT completed the implementation and wrote the manuscript. LP, and KV have reviewed the manuscript.

Corresponding author

Correspondence to Bagyammal Thirumurthy.

Ethics declarations

Conflict of interest

Not applicable.

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Thirumurthy, B., Parameswaran, L. & Vaiapury, K. A deep learning and transfer learning model for intra-change detection in images. J Supercomput 80, 11640–11660 (2024). https://doi.org/10.1007/s11227-023-05878-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05878-w

Keywords

Search

Navigation