期刊
ELECTRONICS
卷 8, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/electronics8050481
关键词
defects recognition; deep learning; regional proposal network; Faster R-CNN
资金
- National Natural Science Foundation of China [51875266]
- Jiangsu Province Graduate Research and Innovation Program [KYCX18-2227]
Machine vision is one of the key technologies used to perform intelligent manufacturing. In order to improve the recognition rate of multi-class defects in wheel hubs, an improved Faster R-CNN method was proposed. A data set for wheel hub defects was built. This data set consisted of four types of defects in 2,412 1080 x 1440 pixels images. Faster R-CNN was modified, trained, verified and tested based on this database. The recognition rate for this proposed method was excellent. The proposed method was compared with the popular R-CNN and YOLOv3 methods showing simpler, faster, and more accurate defect detection, which demonstrates the superiority of the improved Faster R-CNN for wheel hub defects.
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