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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.07694 |
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| _version_ | 1866909222133825536 |
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| author | Akbas, Can Arin, Irem Su Onal, Sinan |
| author_facet | Akbas, Can Arin, Irem Su Onal, Sinan |
| contents | Recent advancements in quality control across various industries have increasingly utilized the integration of video cameras and image processing for effective defect detection. A critical barrier to progress is the scarcity of comprehensive datasets featuring annotated defects, which are essential for developing and refining automated defect detection models. This systematic review, spanning from 2015 to 2023, identifies 15 publicly available datasets and critically examines them to assess their effectiveness and applicability for benchmarking and model development. Our findings reveal a diverse landscape of datasets, such as NEU-CLS, NEU-DET, DAGM, KolektorSDD, PCB Defect Dataset, and the Hollow Cylindrical Defect Detection Dataset, each with unique strengths and limitations in terms of image quality, defect type representation, and real-world applicability. The goal of this systematic review is to consolidate these datasets in a single location, providing researchers who seek such publicly available resources with a comprehensive reference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_07694 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A PRISMA Driven Systematic Review of Publicly Available Datasets for Benchmark and Model Developments for Industrial Defect Detection Akbas, Can Arin, Irem Su Onal, Sinan Computer Vision and Pattern Recognition Machine Learning Recent advancements in quality control across various industries have increasingly utilized the integration of video cameras and image processing for effective defect detection. A critical barrier to progress is the scarcity of comprehensive datasets featuring annotated defects, which are essential for developing and refining automated defect detection models. This systematic review, spanning from 2015 to 2023, identifies 15 publicly available datasets and critically examines them to assess their effectiveness and applicability for benchmarking and model development. Our findings reveal a diverse landscape of datasets, such as NEU-CLS, NEU-DET, DAGM, KolektorSDD, PCB Defect Dataset, and the Hollow Cylindrical Defect Detection Dataset, each with unique strengths and limitations in terms of image quality, defect type representation, and real-world applicability. The goal of this systematic review is to consolidate these datasets in a single location, providing researchers who seek such publicly available resources with a comprehensive reference. |
| title | A PRISMA Driven Systematic Review of Publicly Available Datasets for Benchmark and Model Developments for Industrial Defect Detection |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2406.07694 |