Summary of A Prisma Driven Systematic Review Of Publicly Available Datasets For Benchmark and Model Developments For Industrial Defect Detection, by Can Akbas et al.
A PRISMA Driven Systematic Review of Publicly Available Datasets for Benchmark and Model Developments for Industrial Defect Detection
by Can Akbas, Irem Su Arin, Sinan Onal
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a systematic review of 15 publicly available datasets for automated defect detection from video cameras and image processing. These datasets are essential for developing and refining models that can detect defects effectively. The review assesses the effectiveness and applicability of each dataset, considering factors like image quality, defect type representation, and real-world applicability. Datasets mentioned include NEU-CLS, NEU-DET, DAGM, KolektorSDD, PCB Defect Dataset, and Hollow Cylindrical Defect Detection Dataset. The goal is to consolidate these datasets in a single location, providing researchers with a comprehensive reference for benchmarking and model development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find defects in products using cameras and computer programs. To do this well, you need good examples of what the defects look like. This paper looks at 15 free datasets that have these examples. It’s like a big catalog of pictures and videos that show different kinds of defects. The goal is to put all these datasets together in one place so researchers can easily find what they need. |
