Object Detection and Classification of Metal Polishing Shaft Surface Defects Based on Convolutional Neural Network Deep Learning
| Autor: | Cai Chaopeng, Yanbiao Li, Zheng Qiming, Qingsheng Jiang, Dapeng Tan, Shiming Ji |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Reliability (computer networking) Feature extraction Polishing 02 engineering and technology Convolutional neural network surface defect CNN (Convolutional Neural Network) 0202 electrical engineering electronic engineering information engineering General Materials Science Instrumentation metal shaft Fluid Flow and Transfer Processes business.industry Process Chemistry and Technology Deep learning 020208 electrical & electronic engineering General Engineering deep learning Pattern recognition object detection Image segmentation 021001 nanoscience & nanotechnology Object detection Computer Science Applications Factory (object-oriented programming) Artificial intelligence 0210 nano-technology business |
| Zdroj: | Applied Sciences Volume 10 Issue 1 |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10010087 |
| Popis: | Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection. |
| Databáze: | OpenAIRE |
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