Name
Abstract | ||
|---|---|---|
To ensure the quality of products, it is crucial to inspect and assess their condition in quality control. Among all of the methods, surface inspection is a critical step to identify defective products. With the recent advancement in artificial intelligence and computer vision, a plethora of industries are expecting next level automation. The investments in automated defect detection systems are gaining popularity today as they not only reduce labor costs but also improve the consistency of the production line. This review paper presents some examples of defects in the first part. Then some basic but extensive introduction about industrial camera selection, lens selection, optical illumination are included. Since the images collected from factories are not always satisfying, common methods for image data processing are systematically discussed. Then, this survey comprehensively investigates two neural network algorithms vastly used in industrial object detection systems. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/DSC55868.2022.00091 | 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC) |
Keywords | DocType | ISBN |
industrial defect detection,computer vision,neural network | Conference | 978-1-6654-7481-8 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yunjie Tang | 1 | 0 | 0.34 |
| Kai Sun | 2 | 0 | 0.34 |
| Danhuai Zhao | 3 | 0 | 0.34 |
| Yan Lu | 4 | 0 | 0.34 |
| Jiaju Jiang | 5 | 0 | 0.34 |
| Chen Hong | 6 | 21 | 11.66 |