Defect detection using weakly supervised learning

Sevetlidis, Vasileios; Pavlidis, George; Balaska, Vasiliki; Psomoulis, Athanasios; Mouroutsos, Spyridon; Gasteratos, Antonios

doi:10.1109/ist59124.2023.10355716

2023 IEEE International Conference on Imaging Systems and Techniques (IST) 2023

DOI: 10.1109/ist59124.2023.10355716

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Defect detection using weakly supervised learning

Vasileios Sevetlidis,

George Pavlidis,

Vasiliki Balaska

et al.

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2024

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Cited by 3 publications

References 21 publications

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Dense-PU: Learning a Density-Based Boundary for Positive and Unlabeled Learning

Sevetlidis,

Pavlidis,

Mouroutsos

et al. 2024

IEEE Access

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Dense-PU: Learning a Density-Based Boundary for Positive and Unlabeled Learning

Sevetlidis,

Pavlidis,

Mouroutsos

et al. 2024

IEEE Access

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Enhancing Weakly Supervised Defect Detection Through Anomaly-Informed Weighted Training

Sevetlidis,

Pavlidis,

Balaska

et al. 2024

IEEE Trans. Instrum. Meas.

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Leveraging Positive-Unlabeled Learning for Enhanced Black Spot Accident Identification on Greek Road Networks

Sevetlidis,

Pavlidis,

Mouroutsos

et al. 2024

Computers

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Identifying accidents in road black spots is crucial for improving road safety. Traditional methodologies, although insightful, often struggle with the complexities of imbalanced datasets, while machine learning (ML) techniques have shown promise, our previous work revealed that supervised learning (SL) methods face challenges in effectively distinguishing accidents that occur in black spots from those that do not. This paper introduces a novel approach that leverages positive-unlabeled (PU) learning, a technique we previously applied successfully in the domain of defect detection. The results of this work demonstrate a statistically significant improvement in key performance metrics, including accuracy, precision, recall, F1-score, and AUC, compared to SL methods. This study thus establishes PU learning as a more effective and robust approach for accident classification in black spots, particularly in scenarios with highly imbalanced datasets.

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Defect detection using weakly supervised learning

Cited by 3 publications

References 21 publications

Dense-PU: Learning a Density-Based Boundary for Positive and Unlabeled Learning

Dense-PU: Learning a Density-Based Boundary for Positive and Unlabeled Learning

Enhancing Weakly Supervised Defect Detection Through Anomaly-Informed Weighted Training

Leveraging Positive-Unlabeled Learning for Enhanced Black Spot Accident Identification on Greek Road Networks

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