Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited
AI-oriented quality inspection in manufacturing often faces highly imbalanced data, as defective products are rare, and there are limited possibilities for data augmentation. This paper presents a systematic comparison between Deep Transfer Learning (DTL) and Contrastive Learning (CL) under such cha...
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MDPI AG
2025-05-01
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| author | Amir Farmanesh Raúl G. Sanchis Joaquín Ordieres-Meré |
| author_facet | Amir Farmanesh Raúl G. Sanchis Joaquín Ordieres-Meré |
| author_sort | Amir Farmanesh |
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| description | AI-oriented quality inspection in manufacturing often faces highly imbalanced data, as defective products are rare, and there are limited possibilities for data augmentation. This paper presents a systematic comparison between Deep Transfer Learning (DTL) and Contrastive Learning (CL) under such challenging conditions, addressing a critical gap in the industrial machine learning literature. We focus on a galvanized steel coil quality classification task with acceptable vs. defective classes, where the vast majority of samples (>95%) are acceptable. We implement a DTL approach using strategically fine-tuned YOLOv8 models pre-trained on large-scale datasets, and a CL approach using a Siamese network with multi-reference design to learn robust similarity metrics for one-shot classification. Experiments employ k-fold cross-validation and a held-out gold-standard test set of coil images, with statistical validation through bootstrap resampling. Results demonstrate that DTL significantly outperforms CL, achieving higher overall accuracy (81.7% vs. 61.6%), F1-score (79.2% vs. 62.1%), and precision (91.3% vs. 61.0%) on the challenging test set. Computational analysis reveals that DTL requires 40% less training time and 25% fewer parameters while maintaining superior generalization capabilities. We provide concrete guidance on when to select DTL over CL based on dataset characteristics, demonstrating that DTL is particularly advantageous when data augmentation is constrained by domain-specific spatial patterns. Additionally, we introduce a novel adaptive inspection framework that integrates human-in-the-loop feedback with domain adaptation techniques for continuous model improvement in production environments. Our comprehensive comparative analysis offers empirically validated insights into performance trade-offs between these approaches under extreme class imbalance, providing valuable direction for practitioners implementing industrial quality inspection systems with limited, skewed datasets. |
| format | Article |
| id | doaj-art-4476db11c71f4c63a24f39d0dfa740b5 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-4476db11c71f4c63a24f39d0dfa740b52025-08-20T03:47:58ZengMDPI AGSensors1424-82202025-05-012510304810.3390/s25103048Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is LimitedAmir Farmanesh0Raúl G. Sanchis1Joaquín Ordieres-Meré2Department of Organization Engineering, Business Administration and Statistics, Universidad Politécnica de Madrid, 28040 Madrid, SpainDepartment of Organization Engineering, Business Administration and Statistics, Universidad Politécnica de Madrid, 28040 Madrid, SpainDepartment of Organization Engineering, Business Administration and Statistics, Universidad Politécnica de Madrid, 28040 Madrid, SpainAI-oriented quality inspection in manufacturing often faces highly imbalanced data, as defective products are rare, and there are limited possibilities for data augmentation. This paper presents a systematic comparison between Deep Transfer Learning (DTL) and Contrastive Learning (CL) under such challenging conditions, addressing a critical gap in the industrial machine learning literature. We focus on a galvanized steel coil quality classification task with acceptable vs. defective classes, where the vast majority of samples (>95%) are acceptable. We implement a DTL approach using strategically fine-tuned YOLOv8 models pre-trained on large-scale datasets, and a CL approach using a Siamese network with multi-reference design to learn robust similarity metrics for one-shot classification. Experiments employ k-fold cross-validation and a held-out gold-standard test set of coil images, with statistical validation through bootstrap resampling. Results demonstrate that DTL significantly outperforms CL, achieving higher overall accuracy (81.7% vs. 61.6%), F1-score (79.2% vs. 62.1%), and precision (91.3% vs. 61.0%) on the challenging test set. Computational analysis reveals that DTL requires 40% less training time and 25% fewer parameters while maintaining superior generalization capabilities. We provide concrete guidance on when to select DTL over CL based on dataset characteristics, demonstrating that DTL is particularly advantageous when data augmentation is constrained by domain-specific spatial patterns. Additionally, we introduce a novel adaptive inspection framework that integrates human-in-the-loop feedback with domain adaptation techniques for continuous model improvement in production environments. Our comprehensive comparative analysis offers empirically validated insights into performance trade-offs between these approaches under extreme class imbalance, providing valuable direction for practitioners implementing industrial quality inspection systems with limited, skewed datasets.https://www.mdpi.com/1424-8220/25/10/3048quality inspectionimbalanced datadeep transfer learningcontrastive learningindustrial visionlimited data augmentation |
| spellingShingle | Amir Farmanesh Raúl G. Sanchis Joaquín Ordieres-Meré Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited Sensors quality inspection imbalanced data deep transfer learning contrastive learning industrial vision limited data augmentation |
| title | Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited |
| title_full | Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited |
| title_fullStr | Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited |
| title_full_unstemmed | Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited |
| title_short | Comparison of Deep Transfer Learning Against Contrastive Learning in Industrial Quality Applications for Heavily Unbalanced Data Scenarios When Data Augmentation Is Limited |
| title_sort | comparison of deep transfer learning against contrastive learning in industrial quality applications for heavily unbalanced data scenarios when data augmentation is limited |
| topic | quality inspection imbalanced data deep transfer learning contrastive learning industrial vision limited data augmentation |
| url | https://www.mdpi.com/1424-8220/25/10/3048 |
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