Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Industrial Product Defect Detection Using Machine Learning

Authors
A. Zainab Zaiba1, V. Nivedha Reddy1, B. Kanisha1, *
1Department of Computing Technologies, SRM Institute of Science and Technology, Chennai, India
*Corresponding author. Email: kanishab@srmist.edu.in
Corresponding Author
B. Kanisha
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_262How to use a DOI?
Keywords
Industrial defects; Convolutional Neural Networks (CNNs); ResNet50; VGG16; Mobile Net; machine learning; defect detection
Abstract

Packaging Standards; Packaging quality is key in the modern industrial sector where defects can cost-businesses large sums and harm their brand, image. Conventional ways of inspection are time-consuming, labour intensive and error prone, unsuitable for modern production. The project explores the use of machine learning, more specifically differently structured Convolutional Neural Networks (CNNs), in order to automatically detect defects within industrial packages. Here, we compare three CNN models: ResNet50, VGG16 and Mobile Net using industrial packaging images dataset from Kaggle. As a result, it was found that Mobile Net achieved the best accuracy/speed/compute combination with respect to other models. The system was extended to perform real-time defect detection on video streams, enabling frame-by-frame analysis directly from production footage. In this paper, we discussed the power of CNNs to modernize industrial manufacturing inspection with automatic defect detection and oriented focus towards speed improvement as well.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_262How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - A. Zainab Zaiba
AU  - V. Nivedha Reddy
AU  - B. Kanisha
PY  - 2025
DA  - 2025/11/04
TI  - Industrial Product Defect Detection Using Machine Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 3141
EP  - 3155
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_262
DO  - 10.2991/978-94-6463-858-5_262
ID  - Zaiba2025
ER  -