A Robust Approach for Hair Contaminant Detection in Papadam Using Transfer Learning

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 11

Year of Publication : 2025

Authors : Sarika Panwar, Milind Gajare, Monali Chaudhari, Shravani Doke

10.14445/23488379/IJEEE-V12I11P107

How to Cite?

Sarika Panwar, Milind Gajare, Monali Chaudhari, Shravani Doke, "A Robust Approach for Hair Contaminant Detection in Papadam Using Transfer Learning," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 11, pp. 75-85, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I11P107

Abstract:

Hair contamination in food products is a significant problem that affects consumer confidence, safety, and quality. More sophisticated solutions are required because conventional hair detection methods frequently fail to identify hair particles. The goal of this study is to use deep learning and image processing techniques to create a reliable framework for accurately detecting hair in Papadam. Image masking is used to isolate areas of interest after a thorough preprocessing pipeline that includes greyscale conversion, Wiener filtering for noise reduction, Canny edge detection to highlight edges, and contour detection to extract borderline details. For classification, a pre-trained InceptionV3 transfer learning model is used; custom layers are optimised especially for hair detection, and the initial layers are frozen to retain learnt features. Global Average Pooling 2D and dense layers with ReLU activation are included in the model. Validated using real-world datasets, the suggested method achieved a 97.18% accuracy rate in identifying hair contaminants in Papadam. This study demonstrates how well deep learning and thorough preprocessing work together to improve quality, especially for automatic papadam hair detection.

Keywords:

Hair Detection, Inception-V3, CNN, Transfer Learning.

References:

[1] Keila Payne et al., “Detection and Prevention of Foreign Material in Food: A Review,” Heliyon, vol. 9, no. 9, pp. 1-14, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Darwin G. Caldwell, Automation in Food Manufacturing and Processing, Springer Handbook of Automation, Springer, Cham, pp. 949-971, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] K.K. Patel et al., “Image Processing Tools and Techniques Used in Computer Vision for Quality Assessment of Food Products: A Review,” International Journal of Food Quality and Safety, vol. 1, pp. 1-16, 2015.
[Google Scholar]
[4] Birgit Geueke, Ksenia Groh, and Jane Muncke, “Food Packaging in the Circular Economy: Overview of Chemical Safety Aspects for Commonly Used Materials,” Journal of Cleaner Production, vol. 193, pp. 491-505, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Yu Chen et al., “Low Cost Smart Phone Diagnostics for Food using Paper-Based Colorimetric Sensor Arrays,” Food Control, vol. 82, pp. 227-232, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Hildur Einarsdóttir et al., “Novelty Detection of Foreign Objects in Food using Multi-Modal X-Ray Imaging,” Food Control, vol. 67, pp. 39-47, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Mohammed A. Subhi, and Sawal Md. Ali, “A Deep Convolutional Neural Network for Food Detection and Recognition,” 2018 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES), Sarawak, Malaysia, pp. 284-287, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Yongcai Pan et al., “A Contour Detection Method Based on Image Saliency,” 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), Liuzhou, China, pp. 42-46, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Tulasi Gayatri Devi, and Nagamma Patil, “Analysis &Evaluation of Image Filtering Noise Reduction Technique for Microscopic Images,” 2020 International Conference on Innovative Trends in Information Technology (ICITIIT), Kottayam, India, pp. 1-6, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Mengyang Pu et al., “EDTER: Edge Detection with Transformer,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA, pp. 1392-1402, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Yifan Zhang et al., “Recognition of Foreign Objects in Food Images using Support Vector Machine,” International Conference on Frontier Computing, Singapore, Singapore, pp. 667-674, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Kashish Goyal, Parteek Kumar, and Karun Verma, “Food Adulteration Detection using Artificial Intelligence: A Systematic Review,” Archives of Computational Methods in Engineering, vol. 29, no. 1, pp. 397-426, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Lucas Hansen, and Marco Flôres Ferrão, “Identification of Possible Milk Adulteration Using Physicochemical Data and Multivariate Analysis,” Food Analytical Methods, vol. 11, no. 7, pp. 1994-2003, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[14] I. Gede Iwan Sudipa et al., “Leveraging K-Nearest Neighbors for Enhanced Fruit Classification and Quality Assessment,” Indonesian Journal of Data and Science, vol. 5, no. 1, pp. 30-36, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Alan Inglis et al., “Machine Learning Applied to the Detection of Mycotoxin in Food: A Systematic Review,” Toxins, vol. 16, no. 6, pp. 1-30, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Mahmoud Soltani Firouz, and Hamed Sardari, “Defect Detection in Fruit and Vegetables by using Machine Vision Systems and Image Processing,” Food Engineering Reviews, vol. 14, no. 3, pp. 353-379, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Mukesh Kumar Tripathi, and Dhananjay D. Maktedar, “A Role of Computer Vision in Fruits and Vegetables among Various Horticulture Products of Agriculture Fields: A Survey,” Information Processing in Agriculture, vol. 7, no. 2, pp. 183-203, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Esraa Elhariri et al., “Random Forests Based Classification for Crops Ripeness Stages,” Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014, pp. 205-215, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Zhiyong Zou et al., “Classification and Adulteration of Mengding Mountain Green Tea Varieties based on Fluorescence Hyperspectral Image Method,” Journal of Food Composition and Analysis, vol. 117, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Norbert Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series: with Engineering Applications, MIT Press, 1949.
[CrossRef] [Google Scholar] [Publisher Link]
[21] John Canny, “A Computational Approach to Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679-698, 1986.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Satoshi Suzuki, and KeiichiA be, “Topological Structural Analysis of Digitized Binary Images by Border Following,” Computer Vision, Graphics, and Image Processing, vol. 30, no. 1, pp. 32-46, 1985.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Rafael C. Gonzalez, and Richard E. Woods, Digital Image Processing, 4th ed., Pearson Education, 2019.
[Google Scholar] [Publisher Link]
[24] Yann LeCun et al., “Gradient-Based Learning Applied to Document Recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Jeff Heaton, “Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep Learning,” Genetic Programming and Evolvable Machines, vol. 19, no. 1, pp. 305-307, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Christian Szegedy et al., “Rethinking the Inception Architecture for Computer Vision,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp. 2818-2826, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Sinno Jialin Pan, and Qiang Yang, “A Survey on Transfer Learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010.
[CrossRef] [Google Scholar] [Publisher Link]