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The steel is the core in modern industrial system and its surface quality influences performances and service life of products. In this paper, it is focused on the research on detection technologies for surface defects of steel, systematically introduced common types of surface defects and their formation mechanisms, comprehensively analyzed advantages and disadvantages of traditional detection methods, elaborated the principles, application results and challenges facing of emerging detection technologies represented by machine vision and deep learning as well as deeply discussed the defect repair technologies and surface quality control strategies. The research showed that the detection precision and efficiency were significantly improved with the emerging detection technologies, but they need to be improved from such aspects as adaptability in complicated industrial environment. In the future, the detection technologies for surface defects of steel will be developed towards the directions of multi-technology fusion, intelligence and standardization to meet the increasing industrial production requirements.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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