Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering
In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development of high-quality artificial intelligence solutions...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/10/6234 |
| _version_ | 1797601191290470400 |
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| author | Neven Pičuljan Željka Car |
| author_facet | Neven Pičuljan Željka Car |
| author_sort | Neven Pičuljan |
| collection | DOAJ |
| description | In recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development of high-quality artificial intelligence solutions necessitates a strong emphasis on data-centric approaches that involve the collection, labeling and quality-assurance of data and labels. These processes, however, are labor-intensive and often demand extensive human effort. Simultaneously, there exists an abundance of untapped data that could potentially be utilized to train models capable of addressing complex problems. These raw data, nevertheless, require refinement to become suitable for machine learning training. This study concentrates on the computer vision subdomain within artificial intelligence and explores data requirements within the context of requirements engineering. Among the various data requirement activities, label quality assurance is crucial. To address this problem, we propose a machine learning-based method for automatic label quality assurance, especially in the context of object detection use cases. Our approach aims to support both annotators and computer vision project stakeholders while reducing the time and resources needed to conduct label quality assurance activities. In our experiments, we trained a neural network on a small set of labeled data and achieved an accuracy of 82% in differentiating good and bad labels on a large set of labeled data. This demonstrates the potential of our approach in automating label quality assurance. |
| first_indexed | 2024-03-11T03:57:46Z |
| format | Article |
| id | doaj.art-8086bdf46b3b4c019f7e643b1d5b16e4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:57:46Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8086bdf46b3b4c019f7e643b1d5b16e42023-11-18T00:22:34ZengMDPI AGApplied Sciences2076-34172023-05-011310623410.3390/app13106234Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements EngineeringNeven Pičuljan0Željka Car1Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaFaculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, CroatiaIn recent years, the field of artificial intelligence has experienced significant growth, which has been primarily attributed to advancements in hardware and the efficient training of deep neural networks on graphics processing units. The development of high-quality artificial intelligence solutions necessitates a strong emphasis on data-centric approaches that involve the collection, labeling and quality-assurance of data and labels. These processes, however, are labor-intensive and often demand extensive human effort. Simultaneously, there exists an abundance of untapped data that could potentially be utilized to train models capable of addressing complex problems. These raw data, nevertheless, require refinement to become suitable for machine learning training. This study concentrates on the computer vision subdomain within artificial intelligence and explores data requirements within the context of requirements engineering. Among the various data requirement activities, label quality assurance is crucial. To address this problem, we propose a machine learning-based method for automatic label quality assurance, especially in the context of object detection use cases. Our approach aims to support both annotators and computer vision project stakeholders while reducing the time and resources needed to conduct label quality assurance activities. In our experiments, we trained a neural network on a small set of labeled data and achieved an accuracy of 82% in differentiating good and bad labels on a large set of labeled data. This demonstrates the potential of our approach in automating label quality assurance.https://www.mdpi.com/2076-3417/13/10/6234artificial intelligencecomputer visiondata requirementsdata-centric artificial intelligencedeep learninglabel quality assurance |
| spellingShingle | Neven Pičuljan Željka Car Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering Applied Sciences artificial intelligence computer vision data requirements data-centric artificial intelligence deep learning label quality assurance |
| title | Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering |
| title_full | Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering |
| title_fullStr | Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering |
| title_full_unstemmed | Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering |
| title_short | Machine Learning-Based Label Quality Assurance for Object Detection Projects in Requirements Engineering |
| title_sort | machine learning based label quality assurance for object detection projects in requirements engineering |
| topic | artificial intelligence computer vision data requirements data-centric artificial intelligence deep learning label quality assurance |
| url | https://www.mdpi.com/2076-3417/13/10/6234 |
| work_keys_str_mv | AT nevenpiculjan machinelearningbasedlabelqualityassuranceforobjectdetectionprojectsinrequirementsengineering AT zeljkacar machinelearningbasedlabelqualityassuranceforobjectdetectionprojectsinrequirementsengineering |